Job intelligence
Senior Data Analyst
YipitData
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
The available details do not point to a single standout advantage yet. The clearest issue to weigh is location rules could change the fit.
Current comparable position is around the 39th percentile.
Geographic eligibility is restricted to a country or region.
One or more material qualification requirements are stated and may narrow the realistic applicant pool.
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
- Location eligibility
- Employment type
What to verify next
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
Unknown information is never converted into a negative score. The questions above exist to prevent missing evidence from being mistaken for a favorable term.
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
- Location rules could change the fit: Geographic eligibility is restricted to a country or region.
- The experience requirement raises the entry bar: The listing appears to require at least about 8 years of experience.
Questions worth asking before you invest more time
- Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
- 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.
Senior Data Analyst (MedTech)
YipitData · USA
Why it is nearby: Same seniority band · Same country · Similar work arrangement
About 4% higher advertised midpoint
Senior Data Scientist, Product
RevenueCat · EMEA, LATAM, Canada, USA
Why it is nearby: Same seniority band · Same country · Similar work arrangement
About 22% higher advertised midpoint
Data Scientist, Risk
Imprint · USA
Why it is nearby: Same seniority band · Same country · Similar work arrangement
About 3% higher advertised midpoint
Senior Data Scientist, Full Stack
TRM Labs · Canada, USA
Why it is nearby: Same seniority band · Same country · Similar work arrangement
About 8% higher advertised midpoint
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 (37) · EUR (12) · GBP (6) · CZK (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.
A much stricter subset: only listings with explicit worldwide-remote evidence remain.
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.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
Only same-currency opportunities with usable annualized pay enter this comparison. Other currencies are not converted or treated as lower.
Uses only roles where a country is known. Roles without country evidence are excluded from this denominator.
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 1186 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 29% 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, current same-currency market context, 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 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
- statistical modeling techniques
- perform project management
- query languages
- online analytical processing
- develop data processing applications
- data engineering
- manage data collection systems
Context-dependent ESCO skills
- manage ICT data architecture
- SPARQL
- use spreadsheets software
- computational biology
- state estimation
O*NET · U.S. occupation
Data Scientists
O*NET occupation skills
- Mathematics
- Critical Thinking
- Reading Comprehension
- Complex Problem Solving
- ProgrammingPhrase in listing
- 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
Senior Software Engineer, Security Assurance | UK | Remote
Grafana Labs · Germany, Ireland, Spain, Sweden, UK
same seniority band · same employment type · similar work-arrangement evidenceSenior Software Engineer, Security Assurance | Sweden | Remote
Grafana Labs · Germany, Ireland, Spain, Sweden, UK
same seniority band · same employment type · similar work-arrangement evidenceSenior Software Engineer, Security Assurance | Spain | Remote
Grafana Labs · Germany, Ireland, Spain, Sweden, UK
same seniority band · same employment type · similar work-arrangement evidenceAdjacent-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 Senior / lead. 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
Data Scientist: Product
Thatch · Remote — Worldwide
USD 160,000–230,000 / year Similar work arrangement · Same employment type · Recently posted Advertised midpoint is about 8% higherData Engineer
MicroSourcing · Remote — Worldwide
Similar work arrangement · Same employment type · Recently postedStretch direction
GTM AI Engineer
Customer.io · LATAM, Canada, USA
USD 140,000–170,000 / year Same country · Similar work arrangement · Same employment type Advertised midpoint is about 14% lowerDirector of Product, Machine Learning & Decisioning Platforms
Upstart · Canada, USA
USD 216,100–299,000 / year Same country · Similar work arrangement · Same employment type Advertised midpoint is about 43% higherThese 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?
Databricks 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
- Databricks Seen in 17 sampled listings
- Python Seen in 86 sampled listings
- SQL Seen in 73 sampled listings
Combinations also appearing together
- Python + SQL appeared together in 60 sampled listings
Application edge
- If these skills match your background, prepare one concrete example that shows how you used Databricks, Python, and SQL rather than relying on a keyword-only claim.
- The listing states about 8 years of required experience. Check that the dates and scope in your application make that experience easy to verify.
- The listing mentions extra application steps. Review those requirements before starting so you can prepare the requested material in one pass.
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; 115 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.
At least about 8 years of experience required
The available wording contains an explicit minimum/required years-of-experience signal. Preferred years are tracked separately and never promoted into the required minimum.
Databricks
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.
Python
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.
SQL
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.
Prepare for a stated application step
A portfolio or work sample is explicitly mentioned.
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?
Decision analysis
What the listing really tells you
For Senior Data Analyst at YipitData, the most useful clues right now are pay has a useful comparison point, remote flexibility can be weighed properly, and the listing gives useful clues about who can realistically enter. Looking at them together makes the trade-offs easier to see.
Pay has a useful comparison point
Among 51 current comparable salaries in USD, the normalized annual midpoint is around the 39th percentile. This compares advertised pay in the same currency and does not include every element of total compensation.
Remote flexibility can be weighed properly
The work-location pattern is “Remote with some uncertainty or restrictions”, with Remote Freedom at 62/100. The most important location constraint is geographic eligibility is restricted to a country or region..
The listing gives useful clues about who can realistically enter
At least about 8 years of experience required · 1 additional stated barrier. Only requirements actually stated in the listing are counted; anything left unsaid remains unresolved.
The visible application effort can be estimated
Provider-mediated application path · 1 visible process step. The external application may contain additional steps that are not visible from the listing itself.
Source continuity adds useful context
1 recent enabled source can currently renew this vacancy across 1 source classes. That improves traceability, although it does not verify the employer itself.
How this analysis is assembled
This read combines the structured details currently available for pay, flexibility, qualifications, mobility, application effort and source continuity. Missing facts are left open for you to verify.
Analysis evidence coverage
How much analysis can the current evidence support?
Uses the explainable High Pay Score within the job’s current occupation/seniority/currency comparison sample.
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 source posting date is today.
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.
- Some compensation may be variable or performance-based. Confirm the guaranteed base amount before comparing headline pay.
- The listing appears to require substantial prior experience.
- At least about 8 years of experience required
Still unknown
- Visa sponsorship is not established by the available listing data.
Pay vs. barriers
Pay and barrier context available
The current pay position can be viewed alongside the stated barriers, but the available detail does not support a stronger conclusion.
Comparable disclosed-pay evidence places this job at about 39 / 100 within its current occupation/seniority sample.
1 explicit required barrier is visible in the available wording.
The listing states at least about 8 years of required experience.
1 explicit Remote Freedom restriction signal is present and is shown separately from qualification requirements.
- Education requirement 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: At least about 8 years of experience required · 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 source posting date is today.
Compensation intelligence
How competitive is this pay?
Below many comparable disclosed salaries
The annualized midpoint is below the current comparable median but remains within the sample’s lower-to-middle range.
The highlighted band is the middle 50% of the current comparable sample after the outlier guard. The dark marker is this job’s annualized salary midpoint.
Why this analysis?
- This score reflects the current advertised-pay distribution for comparable active jobs in the same currency. It measures pay position, not overall job quality.
- 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 YipitData
Very strong 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
YipitData — 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: Sales (8) · Product (6) · Data & AI (3)
Visible seniority mix: Senior / lead (20) · Leadership (12)
Current employment-type mix: Full time (34)
Most visible hiring countries in this sample: United States (34)
Other current roles from this employer
Senior Data Analyst (MedTech)
USA
Senior / lead · Region-restricted remoteUSD 175,000–200,000 / yearSenior Business Development Executive - Quant Sales
USA
Leadership · Region-restricted remoteUSD 300,000–300,000 / yearSenior Business Development Executive
USA
Leadership · Region-restricted remoteUSD 150,000–300,000 / yearSenior Account Executive
USA
Leadership · Region-restricted remoteUSD 140,000–150,000 / yearThese are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.
Current disclosed salary medians by currency
- USD 150,000 median · 34 disclosed salaries
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:
- Some compensation may be variable or performance-based. Confirm the guaranteed base amount before comparing headline pay.
- The listing appears to require substantial prior experience.
- 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.
Staff Machine Learning Engineer, Recommendation Systems - Quora
Quora · Canada, Ireland, USA
USD 219,989–320,613 / year Region-restricted remoteWhy comparable: Same seniority band · Same country · Similar work-location arrangement
Lead Machine Learning Engineer
Serve Robotics · Canada, USA
USD 225,000–260,000 / year Region-restricted remoteWhy comparable: Same seniority band · Same country · Similar work-location arrangement
Senior Machine Learning Engineer, Ads - Quora
Quora · Canada, Ireland, USA
USD 189,507–274,604 / year Region-restricted remoteWhy comparable: Same seniority band · Same country · Similar work-location arrangement
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
Visible application steps or requirements
- A portfolio or work sample is explicitly mentioned.
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.
About Us:
YipitData is the leading market research and analytics firm for the disruptive economy and most recently raised $475M from The Carlyle Group at a valuation of over $1B. Every day, our proprietary technology analyzes billions of alternative data points to uncover actionable insights across sectors like software, AI, cloud, e-commerce, ridesharing, and payments.
Our data and research teams transform raw data into strategic intelligence, delivering accurate, timely, and deeply contextualized analysis that our customers—ranging from the world’s top investment funds to Fortune 500 companies—depend on to drive high-stakes decisions. From sourcing and licensing novel datasets to rigorous analysis and expert narrative framing, our teams ensure clients get not just data, but clarity and confidence.
We operate globally with offices in the US (NYC, Austin, Miami, Mountain View), APAC (Hong Kong, Shanghai, Beijing, Guangzhou, Singapore), and India. Our award-winning, people-centric culture—recognized by Inc. as a Best Workplace for three consecutive years—emphasizes transparency, ownership, and continuous mastery.
What It’s Like to Work at YipitData:
YipitData isn’t a place for coasting—it’s a launchpad for ambitious, impact-driven professionals.
From day one, you’ll take the lead on meaningful work, accelerate your growth, and gain exposure that shapes careers.
Why Top Talent Chooses YipitData:
- Ownership That Matters: You’ll lead high-impact projects with real business outcomes
- Rapid Growth: We compress years of learning into months
- Merit Over Titles: Trust and responsibility are earned through execution, not tenure
- Velocity with Purpose: We move fast, support each other, and aim high—always with purpose and intention
If your ambition is matched by your work ethic—and you're hungry for a place where growth, impact, and ownership are the norm—YipitData might be the opportunity you’ve been waiting for.
About The Role:
YipitData’s Corporate team collaborates directly with organizations like Summer Fridays, 3M, and Uber to build one of the most comprehensive views into how consumers in the U.S. are purchasing and engaging with brands. By transforming massive volumes of consumer data into accurate, reliable, and actionable insights, we enable our clients to understand not just what is happening in their businesses, but why—and to use that understanding to make higher-confidence decisions in an increasingly competitive landscape.
Over the years, we’ve delivered high-impact insights across core retail sectors, and our clients have found this data so valuable that demand has expanded into new sectors.To meet this growing demand, we are expanding our Data team within the Corporate practice and are looking for a Senior Data Analyst to lead our new sectors. This role is a unique opportunity to shape how YipitData enters and scales coverage in entirely new verticals.
The Sectors Data team is responsible for taking YipitData’s core retail metrics and making them usable and reliable in new verticals—through improved category classification, more precise product attribution, and continuously increasing data accuracy as coverage evolves.You will work closely with our Insights team to understand what clients in new sectors truly need, then rapidly and iteratively transform the data to deliver those insights You’ll play a critical role in building the methodologies, workflows, and quality standards that allow these sectors to scale.
You’ll report to the Head of Data and operate as a player-coach, balancing hands-on analytical ownership with responsibility for setting standards, improving workflows, collaborating with cross-stakeholders, and coaching analysts on your team.
This is a fantastic opportunity for someone who thrives in ambiguity & rapid growth, enjoys building systems from the ground up, and wants to shape how new sectors are operationalized at scale.
This is a remote-friendly opportunity that can sit in NYC (where our headquarter is located), one of our office hubs (Austin, Miami, Mountain View, or anywhere else in the US. However, depending upon where the remote work is performed, income could be subject to New York State tax withholding.
As Our Emerging Sectors Data Lead, You Will:
- Own data strategy and execution for a portfolio of client deliverables within new sectors, driving product success through a focus on accuracy, client value, and efficiency, and partnering closely with product and revenue leaders to set and hit ambitious targets
- Serve as a subject-matter expert for emerging sector data, partnering with GTM teams on client conversations to explain methodologies, address questions, and ensure our data solutions align with client needs
- Investigate and resolve data quality issues, proactively troubleshooting client- or internal QA concerns, and implementing scalable fixes
- Design, standardize, and maintain scalable workflows, ensuring consistency and efficiency as coverage expands
- Own and evolve sector categorization frameworks, including maintaining and enhancing core sector taxonomies and building custom categorizations for sector-specific clients
- Operate as a player-coach, setting a high bar for quality, speed, and innovation while mentoring and empowering your team to do their best work
- Partner cross-functionally with other teams including Data, Insights, Revenue, and Engineering to uncover and execute on opportunities to further drive ARR growth and optimize our processes and pipelines
You Are Likely To Succeed If:
- You have 8+ years of experience in data analytics, data operations, or consulting, including 2+ years in a lead or management capacity
- You have 4+ years of experience working with SQL, Python, Pyspark, or other programming languages to explore and transform large, complex datasets; Familiarity with Github and Databricks
- You have strong experience working with large, messy, evolving datasets, especially in environments with changing definitions or taxonomies
- You are comfortable with SQL and Python (or similar tools) and have experience building and maintaining production-grade data workflows (Databricks, Spark, GitHub familiarity a plus)
- You have a strong ownership mindset and enjoy building structure in fast-moving environments
- You have strong communication skills and are able to work and project manage effectively with stakeholders across both technical and non-technical teams
What We Offer:
Our compensation package includes comprehensive benefits, perks, and a competitive salary:
- We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
- Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
- The annual on-target earnings for this position is anticipated to be up to $170K ~ $190K + 10% target variable. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks.
- The compensation package also includes equity.
This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have office hubs in Austin, Miami, and Mountain View. If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.
Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity employer.
Listing provenance
Where did this job come from?
See where this vacancy came from and whether more than one current source can still support it. Source names help with traceability and attribution; they are not endorsements or guarantees.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
- Continuity
- One recent renewable source
- Source-class independence
- One renewable source class
- Last source observation
- Observed very recently
This records when High Paid Job last observed the vacancy in source evidence. It confirms source presence, not that the employer edited the vacancy at that time.
- 1 enabled source recently observed this vacancy.
Source confidence is about traceability and continuity. It is separate from application safety and should not be read as an employer reputation rating.
The vacancy is supplied by an external job publisher. Source information helps show where the listing came from; it is not a guarantee about the employer or vacancy.
Listing source: Jobicy. Destination: jobicy.com. The application opens in a new tab so you can keep this report open.