Job intelligence
Staff Data Engineer
Jobber
Before relying on the listing: Confirm Application closing time with the employer or source. See verification questions
Executive opportunity brief
The decision in one screen
A compact evidence-first read of compensation, flexibility, mobility and what still needs verification. No universal quality score is used.
Fast read
Opportunity DNA
This opportunity needs a closer read before drawing a strong conclusion, especially around the salary benchmark is still shallow.
The range is usable, but the comparable sample is still too shallow for a strong relative-pay call.
Geographic eligibility is restricted to a country or region.
Some access and qualification signals are clear, but the overall entry path is neither unusually open nor unusually restrictive.
Sponsorship and relocation support are not clear from the current listing.
Part of the application route can be assessed, but the full external process may contain additional steps.
At least one recent enabled source can still renew this vacancy.
This fingerprint summarizes what is clear, constrained or still unanswered in the current listing. It is a reading aid, not an overall job score.
Decision evidence check
4 of 5 core facts stated
This checks whether the listing states the facts that commonly change an employment decision. It measures disclosure completeness, not whether the opportunity is good or bad.
Facts already stated
- Compensation
- Work arrangement
- Advertised location
- Employment type
What to verify next
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
Advertised location is not proof of work authorization, sponsorship, or legal eligibility. Unknown information is not a negative score; verify those questions separately.
Decision levers
What could make or break the fit?
Reasons to look closer
Nothing stands out strongly enough yet to call a clear advantage. The role may still be attractive for reasons the listing does not spell out.
Trade-offs to weigh
- The salary benchmark is still shallow: Pay is disclosed, but the current comparison pool is too small for a strong relative-pay conclusion.
- Location rules could change the fit: Geographic eligibility is restricted to a country or region.
Questions worth asking before you invest more time
- Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
- Confirm the actual minimum experience requirement rather than assuming preferred background is mandatory.
- Ask whether visa sponsorship is available before investing significant time in the application.
These questions come from details the listing leaves open. An unanswered point is not treated as a disadvantage.
Opportunity neighborhood
Keep your options open
These live roles sit close enough to this opportunity to make useful comparison points. Look across several employers before deciding what pay, flexibility or access trade-offs you are willing to accept.
Machine Learning Engineer, Voice Conversion
Cantina Labs · Europe, USA
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit · Different country
Director of Technology Data and Artificial Intelligence
Department of the Air Force Headquarters · Pentagon, Arlington, Virginia
Why it is nearby: Same seniority band · Recently posted
Different angle: Different country
Applied AI Engineer
Bjak · Germany
Why it is nearby: Same employment type · Recently posted
Different angle: Seniority is not explicit · Different country
Data Analyst
Agile Resources AB · Solna, Stockholms län, Sverige
Why it is nearby: Same employment type · Recently posted
Different angle: Seniority is not explicit · Different country
This neighborhood is drawn from current inventory in the same broad occupation. Similarity is based on explicit listing facts; missing details do not count against a job.
Role market context
How much current choice is visible around this kind of work?
This compares the vacancy with current High Paid Job inventory in the broad Data & AI roles family. It describes visible choice and disclosure, not future demand or career quality.
200 most recent current roles sampled for the disclosure measures below.
Distinct employers in the bounded current sample, not an estimate of all employers hiring in the market.
Current sampled roles stating at least four of the five Core Decision Facts.
Requires a numeric amount, explicit ISO currency and supported pay period.
Listings with explicit enough remote, hybrid or on-site evidence to classify.
Missing deadline evidence remains unknown and is not interpreted as an open-ended application window.
Currencies with explicit pay evidence in the sample: USD (47) · EUR (15) · GBP (2) · CAD (1). Raw amounts across different currencies are not compared.
This describes current visible High Paid Job inventory in the same broad occupation family. Percentages use a bounded recent sample and are not a forecast, a complete labor-market census or an employer-quality measure.
Constraint opportunity lab
What changes the size of the current opportunity pool?
Each line applies one stricter evidence-based constraint to the same bounded current role-family sample. It helps you see the trade-off in visible choice without deciding which trade-off is right for you.
Counts only affirmative or possible sponsorship evidence, or relocation explicitly offered. Unknown is not treated as no.
Uses only roles where a country is known. Roles without country evidence are excluded from this denominator.
A much stricter subset: only listings with explicit worldwide-remote evidence remain.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
Shows the subset with enough stated evidence for a more informed first comparison.
Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.
Includes roles whose seniority is genuinely unspecified rather than assuming they belong to another level.
Uses only roles with a stated employment type.
These are counterfactual filters over a bounded sample of current HPJ inventory in the same broad occupation family. They show how visible choice changes when one evidence-based constraint is applied; they do not tell you which constraint you should choose or predict hiring outcomes. The calculation uses the 200 most recent current roles from 1250 visible roles in this broad family.
Salary transparency context
Is pay disclosure typical for this kind of role?
This listing states usable structured compensation. About 33% of the bounded current role sample does the same.
Compensation counts only when amount, ISO currency and supported pay period are all known.
Pay disclosure is limited in the current role sample.
This employer’s current visible listings disclose pay materially more often than the current same-role sample.
This compares salary-disclosure behavior in current visible listings. It does not compare raw salary amounts across currencies, infer undisclosed pay as zero, or rate employer quality.
Private offer & negotiation lab
Turn an offer into evidence, not guesswork
Enter an annual base offer and, if you want, your own target. HPJ compares only within one explicit currency and never chooses a target for you.
Negotiation evidence available: advertised pay range, strong decision evidence.
Enter an annual base offer to compare it with explicit same-currency evidence. HPJ will not suggest a target for you.
Review the full package · 10 terms
Mark what is confirmed, what needs clarification and what you want to prioritize. These choices and notes stay in this browser.
Usable advertised compensation is available.
Variable compensation is mentioned; verify amount, formula, target and eligibility.
Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.
Retirement or pension benefits are mentioned; verify employer contribution and vesting.
Health-benefit language is mentioned; verify coverage, dependants and employee cost.
Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.
A work-arrangement signal is available; verify schedule details before relying on it.
Relocation support not established
Sponsorship not established
A review cycle is mentioned; verify timing and whether compensation can change at that review.
Questions worth taking into the offer conversation
- Is there a bonus, commission or other variable-pay component, and how is it calculated?
- What retirement or pension contribution does the employer provide?
- What health coverage is included and what costs remain with the employee?
- How much paid leave is provided and what are the carry-over or blackout rules?
- If relocation is relevant, what expenses, timing and repayment conditions are covered?
- What exact sponsorship or work-authorization conditions apply to this role?
- When is the first performance and compensation review after joining?
- Is there a firm application deadline, a rolling review window or no stated closing date?
Occupation intelligence
What broader career references connect to this role?
HPJ uses broad comparison families for current-market analysis. External taxonomy results are reference candidates for that broad family, not a declaration that this vacancy has been authoritatively classified into a specific occupation.
European occupation references
This service uses the ESCO classification of the European Commission.
U.S. occupation references
- Data Scientists 15-2051.00
- Computer and Information Research Scientists 15-1221.00
- Social Science Research Assistants 19-4061.00
- Atmospheric and Space Scientists 19-2021.00
- Bioinformatics Scientists 19-1029.01
This site incorporates information from O*NET Web Services by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). O*NET® is a trademark of USDOL/ETA.
Career reference research
Skills worth investigating
Official occupational context, paired with exact phrases visible in this vacancy. It is not a skills test or an applicant-fit score.
ESCO · European occupation
data scientist
Usually essential in this ESCO occupation
- handle data samples
- integrate gender dimension in research
- deliver visual presentation of data
- scientific literature
- promote the participation of citizens in scientific and research activities
- perform data cleansing
- manage intellectual property rights
Context-dependent ESCO skills
- MDX
- scientific computing
- digital curation
- integrate ICT data
- state estimation
O*NET · U.S. occupation
Data Scientists
O*NET occupation skills
- Mathematics
- Critical Thinking
- Reading Comprehension
- Complex Problem Solving
- Programming
- Active Learning
- Speaking
- Active Listening
Build my private skill preparation brief Your evidence · Practice · Interview questions
Plan how you might demonstrate or investigate occupational skills. These are broad-family reference examples, not confirmed vacancy requirements. Nothing you enter is sent to High Paid Job.
Private to this browser. Up to 20 reference skills; not a qualification assessment or hiring prediction.
Useful next question: Which of these occupational reference skills does the hiring manager actually expect for this specific role, and what evidence would demonstrate them?
These skills belong to one reference occupation selected for HPJ’s broad career family, not a verified classification of this vacancy. Text overlap only means a phrase appears in the available listing text; it does not establish an employer requirement, your proficiency, or a qualification gap. A missing mention is unknown, not a deficit.
Adjacent current roles to investigate
GTM Enablement - Product
ElevenLabs · Remote
same employment type · similar work-arrangement evidenceFull-Stack Engineer - ElevenAPI
ElevenLabs · Remote
same employment type · similar work-arrangement evidenceFull Stack utvecklare
ELK Studios AB · Stockholm, Stockholms län, Sverige
same employment typeAdjacent-family roles are exploration prompts from current HPJ inventory. They are not inferred qualification matches and they do not imply a promotion path.
Career move map
What sits one step away?
This listing is currently read as Leadership. Nearby roles one visible seniority step away can help you judge whether to widen the search, stretch upward or compare the responsibilities more carefully.
Broader-entry direction
Senior Data Scientist - Personalization & Predictions
Bloomreach · Czechia, Slovakia
Similar work arrangement · Same employment type · Recently postedSenior Data Analyst
Tremendous · LATAM, Canada, USA
Similar work arrangement · Same employment type · Recently postedThese are live roles one visible seniority step away in the same broad occupation family. They can reveal nearby options, but they are not a guaranteed promotion path and do not account for your personal experience.
Skill bridge
What does this role connect to?
AWS is one of the signals this listing shares with skills appearing often across current Data & AI roles opportunities.
Skills named in this listing
Shared with the current career sample
- AWS Seen in 29 sampled listings
- Machine Learning Seen in 51 sampled listings
- Snowflake Seen in 18 sampled listings
Additional signals in this listing: React · Terraform · TypeScript
Application edge
- If these skills match your background, prepare one concrete example that shows how you used AWS, Machine Learning, and React rather than relying on a keyword-only claim.
If the named skills are genuinely transferable in your own experience, the occupation map also points to adjacent current families worth researching: Software engineering roles and Product roles. This is exploration context, not a qualification match.
Skill signals come from explicit text mentions. A mention is not automatically a requirement, and absence from the current top-skill list does not make a skill rare or unimportant. The current career comparison draws on 120 sampled listings; 119 contain a recognized skill signal.
Application Decision Lab
Turn the listing into a preparation plan
Separate what the listing actually states from what you still need to prepare or verify. Your selections and short notes stay in this browser and are never submitted to High Paid Job.
Non-standard schedule requirement stated
The available wording indicates night, weekend, rotating-shift or on-call work.
AWS
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Machine Learning
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
React
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Snowflake
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Terraform
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
TypeScript
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Questions worth resolving before you rely on the opportunity
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
- Work authorization / sponsorship: If sponsorship or work authorization matters to you, what exact immigration or employer-sponsorship conditions apply to this role?
- Application process: What interview, assessment, portfolio, reference or screening stages are part of the hiring process?
Decision analysis
What the listing really tells you
Staff Data Engineer at Jobber becomes easier to judge once pay is visible, but the benchmark is still thin, work-location flexibility has usable detail, and the entry path is becoming clearer are viewed together. Here is the current read from the information available.
Pay is visible, but the benchmark is still thin
The CAD range can be compared directly within its currency, although the present comparison sample is too shallow for a confident relative-pay ranking.
Work-location flexibility has usable detail
Location flexibility can be assessed here: Remote with some uncertainty or restrictions, with a Remote Freedom reading of 62/100. One practical limit to notice is geographic eligibility is restricted to a country or region..
The entry path is becoming clearer
1 additional stated barrier. Unstated education or experience requirements stay open rather than being assumed.
The application route gives a few useful clues
Provider-mediated application path. The external application may contain additional steps that are not visible from the listing itself.
The listing can be traced to a current source
The opportunity currently has 1 renewable source path across 1 source classes. Multiple independent paths can strengthen continuity, but they are not a substitute for checking the application destination.
How this analysis is assembled
Built from the pay, location, qualification, mobility, application and source details available for this vacancy. Anything the listing leaves unresolved stays unresolved.
Analysis evidence coverage
How much analysis can the current evidence support?
Only 0 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
The configured source supplied structured salary fields, a usable currency and a focused comparison value.
The listing was posted about 2 days ago.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
There is no universal opportunity score because pay, flexibility and access matter differently to different people. Compare Jobs lets you weight the evidence around your own priorities.
Decision checklist
What is supported, uncertain or worth checking?
This checklist separates supported strengths, visible cautions and missing evidence. It is not an overall recommendation or guarantee that the job is suitable for you.
Supported strengths
No standout strength is supported strongly enough to list here. That is not a negative rating.
Visible cautions
- Geographic eligibility is restricted to a country or region.
- The role may involve non-standard hours or on-call work.
- This is remote work with a geographic restriction, not a worldwide-remote vacancy.
- Non-standard schedule requirement stated
Still unknown
- There is not enough comparable salary evidence to establish this job’s current pay position.
- Visa sponsorship is not established by the available listing data.
Pay vs. barriers
Opportunity Efficiency not assessed
The current pay position is not reliable enough to weigh compensation against the stated barriers.
1 explicit required barrier is visible in the available wording.
1 explicit Remote Freedom restriction signal is present and is shown separately from qualification requirements.
- Current comparable pay position is unavailable.
- Education requirement is not established.
- Required prior-experience level is not established.
This section weighs pay context against clearly stated barriers without collapsing unlike requirements into one universal score. Degree, experience, licensing, clearance and location limits remain separate, and missing qualification details stay unknown.
Beyond the headline
Not enough evidence for a hidden-value finding
Potential hidden value appears only when several distinct non-pay advantages are actually supported. Missing requirements or benefits are never counted as positives.
Still check: Non-standard schedule requirement stated · The current application path is provider-mediated rather than directly classified as an employer-authorized or official route.
Hidden Value is an experimental evidence summary, not an investment-style score or guarantee of employability. It only uses explicit current listing evidence and does not infer that an unmentioned qualification is unnecessary.
Explainable opportunity highlight
Why this opportunity stands out
These are supported signals from the current listing and comparison sample—not a guarantee that this is the best job for you.
The source provides structured salary information with a comparatively focused range, improving the usefulness of pay comparisons.
The listing was posted about 2 days ago.
Compensation intelligence
How competitive is this pay?
Only 0 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
Why this analysis?
- Only 0 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
- Salary confidence: High. The configured source supplied structured salary fields, a usable currency and a focused comparison value.
- Range quality: Focused salary range.
Employer compensation communication
Pay transparency at Jobber
Good current pay transparency
This score uses the employer’s current visible listings and combines how often pay is disclosed with how useful those ranges are. Bonus, equity and benefit mentions are shown separately and do not raise the score.
This pattern comes from the employer’s current visible vacancies. It describes hiring and pay transparency in those listings, not employee satisfaction or a permanent judgement of the employer.
Current employer intelligence
Jobber — current opportunity snapshot
Employer Evidence Profile
How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.
Current sampled listings with advertised compensation evidence.
Current sampled listings whose work arrangement is clearer than unknown.
Current sampled listings with a supported country signal.
Current sampled listings with an explicit closing time.
Current sampled listings with a usable posting date.
Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.
Current sampled listings with an explicit relocation signal.
Low coverage means the current visible listings state the information less often. It does not prove that the employer lacks the policy, benefit or support.
Common current occupation families: Software engineering (2) · Data & AI (1) · Executive (1)
Visible seniority mix: Leadership (8)
Current employment-type mix: Full time (8)
Most visible hiring countries in this sample: Canada (5) · United States (3)
Other current roles from this employer
Principal Engineer (AI, Office of the CEO)
USA
Leadership · Region-restricted remoteSenior Manager, Credit Risk
Canada
Leadership · Region-restricted remoteGTM Operations Lead
Canada
Leadership · Region-restricted remoteManager, Software Engineering, Operations
Canada, USA
Leadership · Region-restricted remoteThese are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.
There are not yet five current disclosed salaries in one currency, so an employer pay median would be too thin to be useful.
Questions worth asking this employer
- If work authorization matters to you, what sponsorship policy applies to this specific role and location?
- If a move is required, is relocation assistance available for this position?
- What is the expected application and interview timeline if the listing does not state a closing date?
These questions are prompted by missing or thin evidence in the employer’s current visible vacancies. Missing evidence is not treated as a negative judgement.
This employer snapshot uses the employer’s current visible listings. Remote and sponsorship rates use only vacancies with explicit evidence, while missing information stays unknown. Salary medians are shown separately by currency and need at least five disclosed salaries. Zero confirmed sponsorship means none was explicitly found in this sample, not that sponsorship is unavailable.
This snapshot describes the employer’s current visible vacancies—things such as pay disclosure and work arrangement. It is not an employee-satisfaction or hiring-quality rating, and different currencies stay separate.
Work-location intelligence
Remote Freedom
Remote with some uncertainty or restrictions
Why this score?
- The role is remote but the source identifies a country or regional restriction.
Location restrictions detected
- Geographic eligibility is restricted to a country or region.
Remote Freedom measures observable location flexibility only. Unknown work-location information remains unscored. Timezone wording is penalized only when it appears restrictive; general collaboration across time zones is not treated as a restriction. A score does not guarantee legal work eligibility, payroll availability or visa sponsorship.
International applicant intelligence
Visa & relocation signals
Sponsorship status is shown only when the listing provides clear enough wording. Employer history or headquarters location is not used to guess an answer.
The available listing details do not contain a clear enough sponsorship statement.
The available listing details do not contain a clear enough relocation statement.
Tradeoff check
What’s the catch?
These are visible conditions worth checking before treating the headline compensation as the whole story:
- The role may involve non-standard hours or on-call work.
- This is remote work with a geographic restriction, not a worldwide-remote vacancy.
Current alternatives
Better-paying alternatives
These current roles show a materially higher annualized salary midpoint in the same broad occupation and currency. Similar seniority, country, work arrangement, employment type and recency are preferred where possible; higher pay alone does not make an alternative a better fit.
Analytics Engineer, Data
Tailscale · Canada
Region-restricted remoteWhy comparable: Same country · Similar work-location arrangement · Same employment type
Check: Seniority is not explicit in the alternative listing.
Alternatives are drawn from the current inventory rather than presented as application recommendations. A pay difference needs to be meaningful—about 3% or more at the midpoint—and different currencies are not compared.
Application-path intelligence
What can we see about the application process?
Provider-mediated application path
The current application path comes through a publisher or aggregator. The destination may still lead to an employer-authorized hiring system, but that is not clear enough to assume.
Current destination: jobicy.com · HTTPS
No additional required application step is clear in the available listing text. This does not mean the external application is one-click or friction-free.
Application effort is estimated from visible listing and source details. Optional steps are kept separate from mandatory ones, and the external form may contain additional requirements that are not visible here.
Application safety context
Application safety check
Basic application-safety context available
The current application destination has a basic transport-safety signal such as HTTPS. This does not verify the employer, vacancy, recruiter or application process.
Evidence checked: Source provenance · Application destination · Listing wording
Observable transport-safety signals
- The available application destination uses HTTPS.
Before you share money or sensitive information
- Confirm the vacancy on the employer’s official careers site when practical, especially if a recruiter contacts you outside the listed application path.
- Check that recruiter email domains and application destinations are consistent with the employer or an identifiable recruiting provider.
- Do not pay application, processing, training or equipment fees merely to be considered for a job.
- Do not send cryptocurrency, gift cards, online-banking credentials or PINs as part of an application.
- Treat unusually fast offers or interviews conducted only through informal messaging channels as reasons to verify the employer independently.
This is a limited safety screen, not employer verification and not a fraud determination. Never send money, cryptocurrency, passwords or sensitive banking credentials merely because a listing appears on a job platform.
Original listing details
The section below comes from the listing source. The decision sections above use the job’s structured pay, location, qualification, mobility and application details to give you additional context.
Please note: Our Talent team at Jobber will be offline for Jobber Impact - our flagship event of the year, bringing Jobberinos together to connect, learn, and celebrate what we're building - starting October 6th and extending until October 9th. We’ll be back to go through applications and reach out with an update on your candidacy once we return next week!
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This will be updated automatically on all job posts above our What you can expect from Jobber headline.
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The automated email to candidates will have the same message as above.
Does working with Data motivate and excite you? Do you want to make a difference cross functionally?
Then Jobber might be the place for you! We’re looking for a new Staff Data Engineer to join our Data Platform Team.
Jobber exists to help people in small businesses be successful. We work with small home service businesses, like your local plumbers, painters, and landscapers, to transform the way service is delivered through technology. With Jobber they can quote, schedule, invoice, and collect payments from their customers, while providing an easy and professional customer experience. Running a small business today isn’t like it used to be—the way we consume and deliver service is changing rapidly, technology is evolving, and customers expect more. That’s why we put the power and flexibility in their hands to run their businesses how, where, and when they want!
Our culture of transparency, inclusivity, collaboration, and innovation has been recognized by Great Place to Work, Canada’s Most Admired Corporate Cultures, and more. Jobber has also been named on the Globe and Mail’s Canada’s Top Growing Companies list, and Deloitte Canada’s Technology Fast 50™, Enterprise Fast 15, and Technology Fast 500™ lists. With an Executive team that has over thirty years of industry experience of leading the way, we’ve come a long way from our first customer in 2011—but we’ve just scratched the surface of what we want to accomplish for our customers.
We help employees grow professionally; we have a ton of onboarding resources, tutorials, hackathons and buddies to support learnings and provide opportunities to innovate. We have a range of experience levels on teams which allows for mentor/mentee opportunities. Leaders at Jobber work with empathy and support employees to build healthy work-life harmony. Bring your dedication and passion to this job to fulfill your goals.
The team:
The Data Integration team's mission is to empower teams across Jobber with the right data, at the right time, in the right place, so they can deliver business value better and faster. Key responsibilities include: data ingestion and Change Data Capture (CDC), including materializing CDC streams into tables in Snowflake; data activation (egress); managing the systems involved in the movement and transformation of data; self-serve tooling; and data integrity and governance.
The role:
As a Staff Data Engineer, you will play a critical role in shaping the future of data integration at Jobber. As a technical champion and force multiplier, you’ll lead and mentor a team of exceptional data engineers while solving complex technical challenges. Your expertise will span architecture, technical leadership, design, and hands-on coding, enabling you to significantly influence the direction of data at Jobber.
Beyond day-to-day delivery, you will dedicate time to work acceleration, cross-team initiatives, exploration of emerging technologies, addressing technical debt, and investing in the future of engineering at Jobber. This is a highly strategic and hands-on position where you’ll combine deep technical expertise with leadership to design resilient systems, empower teams across the organization to self-serve with confidence, and ensure our data remains a trusted asset that accelerates business growth.
As a Staff Data Engineer, you will:
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Shape Foundational Data Components: Design, build, and maintain scalable batch and real-time data pipelines, while also looking across systems and ahead to anticipate future needs.
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Demonstrate Technical Mastery: Deliver high-quality solutions through deep expertise in modern data tools and technologies. Champion technical excellence within the team by setting best practices, raising the bar for engineering quality, and mentoring team members at all levels to support their growth and career development.
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Drive Reliability & Resilience: Establish testing and reliability standards, SLAs (uptime, RTO, RPO), and disaster recovery playbooks. Lead major reliability initiatives to minimize downtime and protect critical business data.
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Advance Observability & Governance: Build frameworks for monitoring, logging, lineage, and auditing to ensure visibility, compliance, and trust in data. Define governance policies that enforce data integrity, availability, and reliability across the platform.
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Accelerate and Empower Data Access: Develop self-service tools, frameworks, and automation that reduce manual effort, improve efficiency, and enable teams across engineering, analytics, and data science to work effectively with data while minimizing dependency on the Data Platform team.
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Contribute to Strategic Planning: Partner with Technical Program Managers to define and refine strategic roadmaps, ensuring that data engineering priorities align with business objectives.
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Drive Cross-Team Collaboration: Collaborate with Staff Engineers and technical leaders across domains to identify friction points, and work collectively to design solutions that improve system reliability, consistency, and scalability.
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Accelerate Business Growth: Work closely with data analysts, scientists, and product teams to enable fast, seamless exploration, analysis, modeling, and reporting. Build automation and infrastructure that reduce friction and accelerate decision-making.
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Safeguard Data Integrity: Own the integrity and reliability of data, ensuring stakeholders across the organization maintain trust in the insights and decisions driven by it.
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On-Call Rotation: Members of the Data Platform team participate in an on-call rotation, covering one week at a time. When an incident occurs outside of regular working hours, we provide time off in lieu to support healthy balance and recovery. From time to time, major maintenance work may require team members to serve as primary or secondary on-call support over a weekend.
Jobber is committed to maintaining a fair, transparent, and humane on-call experience. Your interview team will be happy to walk you through how we manage on-call responsibilities in practice.
To be successful, you should have:
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Core Data Engineering Expertise: Hands-on experience with batch and real-time data processing frameworks, lakehouse/warehouse management, large-scale data transformation, data serialization, workflow orchestration and dimensional modeling (star/snow-flake schemas)
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Scalable Systems Development: Proven ability to design and deliver highly scalable, maintainable, and high-performance solutions across multiple layers of the technology stack, leveraging containerization, CI/CD, and API development.
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AWS Cloud Proficiency: Strong understanding of AWS services relevant to the data domain, with hands-on experience leveraging them to design and implement data solutions.
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Technical Leadership & Engineering Excellence: Demonstrated success guiding teams through complex, high-impact projects while providing architectural direction and serving as a trusted technical lead. Exceptional proficiency in software design, system architecture, and coding, with a focus on long-term maintainability, performance, and resilience.
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Reliability & DevOps Practices: Strong background in infrastructure-as-code (Terraform, CloudFormation), observability (logging, monitoring, tracing), and system reliability.
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Collaboration & Adaptability: Exceptional communication skills, self-motivation, and resourcefulness, with the ability to navigate ambiguity, prioritize effectively, and deliver results in fast-paced environments.
It would be really great (but not a deal-breaker) if you had:
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Machine Learning Platform Experience: Exposure to ML platforms and distributed compute frameworks (e.g., Ray, TensorFlow, PyTorch). Experience collaborating with Data Scientists to operationalize models, implement drift detection, or scale ML workloads.
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Cross-Domain Engineering Experience: Hands-on exposure to non–data engineering codebases, such as web application frameworks (Ruby on Rails) and modern front-end stacks (TypeScript/React).
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API & Integration Knowledge: Familiarity with GraphQL, API layer design, and performance optimization.
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Platform Building Experience: Prior work on developer tooling or shared platforms that supported multiple engineering domains.
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Governance & Compliance Awareness: Knowledge of data privacy, security, and compliance in cloud-based data environments.
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Knowledge of data privacy, security, and compliance considerations in a cloud-based data environment.
Compensation:
At Jobber, we also believe that compensation should be transparent, fair, and supportive of your experience and growth. This role has a minimum annual salary of $169,200 CAD, a midpoint of $199,100 CAD and a maximum salary of $228,900 CAD, designed to reflect the progression from learning the ropes to truly excelling.
We design our compensation to reflect each new hire's skills, experience, and the complexity of the role, ensuring a fair and competitive salary. Our range is intentionally broad to support growth and long-term impact, with fully established hires typically starting around the midpoint. The higher end of the range is reserved for those who have demonstrated deep expertise and lasting contributions, while offers below the midpoint reflect strong potential with room to develop. This approach ensures that compensation aligns with both an individual's current capabilities and their opportunity for future growth.
Base salary is just one part of a total compensation package that will include equity rewards, annual stipends for health and wellness, retirement savings matching, and an extended health package with fully paid premiums for body and mind. Your professional growth matters to us too! You'll have access to a dedicated talent development program that includes career coaching and opportunities for career development.
We believe in transparency and open conversations about compensation. If you have any questions about our approach, we're happy to discuss them throughout the hiring process!
What you can expect from Jobber:
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A total compensation package that includes an extended health benefits package with fully paid premiums for both body and mind, matching in RRSP, TFSA or FHSA, and stock options.
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A dedicated Talent Development team and access to coaching, learning, and leadership programs to help you grow your career, reach your goals, and unlock your full potential.
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A unique opportunity to build, grow, and leave your impact on a $400-billion industry that has no dominant player...yet.
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To work with a group of people who are humble, supportive, and give a sh*t about our customers.
We believe that diverse teams perform better and that fostering an inclusive work environment is a key part of growing a successful team. We welcome people of diverse backgrounds, experiences, and perspectives. We are an equal opportunity employer, and we are committed to working with applicants requesting accommodation at any stage of the hiring process.
A bit more about us:
Job by job, we’re transforming the way service is delivered. Your lawn care provider, home cleaning service, plumber or painter could use Jobber to better connect with their customers, save time in the office, invoice faster, and get paid! We’re bringing tens of thousands of people together with technology to deliver billions of dollars a year in services to happy customers. Jobber exists to help make these small businesses successful, and when they’re successful we all win!
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