Job intelligence
Senior Data Engineer
Valtech
Before relying on the listing: Confirm Compensation 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
There is a useful advantage here: location flexibility looks unusually broad. Still, the pay picture is incomplete could change the fit.
A financial comparison will need the approved compensation range.
No material geography, office-attendance or timezone restriction is visible in the available wording.
The listing does not make the minimum education or experience threshold clear enough yet.
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
- Work arrangement
- Advertised location
- Application closing time
- Employment type
What to verify next
- Compensation: What is the approved compensation range, and what portion is guaranteed base pay rather than variable compensation?
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
- Location flexibility looks unusually broad: The available wording supports high Remote Freedom without a material geography, office-attendance or timezone restriction showing up.
Trade-offs to weigh
- The pay picture is incomplete: No usable salary range is stated, so a meaningful financial comparison is not possible yet.
Questions worth asking before you invest more time
- Ask for the approved compensation range before comparing this opportunity financially.
- 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.
Founding Machine Learning Engineer, Computer Vision (Object Detection)
Clera · Remote — Worldwide
Why it is nearby: Same seniority band · Similar work arrangement · Same employment type
Staff Data Engineer
Sparta Commodities · London
Why it is nearby: Same seniority band · Same employment type · Recently posted
Different angle: Different work arrangement
Lead Analytics Engineer - Finance Data (FinOps)
Finom · United Kingdom
Why it is nearby: Same seniority band · Same employment type · Recently posted
Different angle: Different work arrangement
Senior Data Engineer
Accurx · London (Shoreditch)
Why it is nearby: Same seniority band · Same employment type · Recently posted
Different angle: Different work arrangement
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 (24) · EUR (11) · GBP (4) · PLN (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.
Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.
Shows the subset with enough stated evidence for a more informed first comparison.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
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 1215 visible roles in this broad family.
Salary transparency context
Is pay disclosure typical for this kind of role?
This listing does not state usable structured compensation. About 20% of the bounded current role sample does.
Compensation counts only when amount, ISO currency and supported pay period are all known.
Pay disclosure is uncommon in the current role sample.
This employer’s current pay-disclosure rate is broadly similar to 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: 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.
The approved base-pay range is not established in the listing.
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
- What is the approved base-pay range for this role?
- Is there a bonus, commission or other variable-pay component, and how is it calculated?
- Does the package include equity or another long-term incentive, and what are the vesting terms?
- What retirement or pension contribution does the employer provide?
- 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?
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
- statistics
- think abstractly
- apply research ethics and scientific integrity principles in research activities
- integrate gender dimension in research
- promote the participation of citizens in scientific and research activities
- communicate with a non-scientific audience
- implement data quality processes
Context-dependent ESCO skills
- Hadoop
- unstructured dataPhrase in listing
- apply blended learning
- teach in academic or vocational contexts
- digital curation
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 Salesforce Developer, Field Service
Omnidian · Anywhere in the World, 🇮🇳 India
same seniority band · same employment type · similar work-arrangement evidenceSenior Backend Engineer - Backend Platform (USA Only, 100% Remote)
Close · Anywhere in the World, 🇺🇸 United States of America
same seniority band · same employment type · similar work-arrangement evidenceDevOps Engineer (Akamai/Terraform) - Portugal Full Remote
HumanIT Digital Consulting · Remote — Worldwide
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 Engineer & PowerBI Developer
LUZA Group · Remote — Worldwide
Similar work arrangement · Same employment type · Recently postedData Analyst
Naples Jet Center · Remote — Worldwide
Similar work arrangement · Same employment type · Recently postedStretch direction
Staff Analytics Engineer
Monzo · UK
Similar work arrangement · Same employment type · Recently postedAnalytics Engineer
Alma · France
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 37 sampled listings
- Azure Seen in 19 sampled listings
- Databricks Seen in 20 sampled listings
- Google Cloud Seen in 24 sampled listings
Combinations also appearing together
- Python + SQL appeared together in 65 sampled listings
- Machine Learning + Python appeared together in 34 sampled listings
- AWS + Python appeared together in 31 sampled listings
Additional signals in this listing: Agile · Apache Spark · Generative AI
Application edge
- If these skills match your background, prepare one concrete example that shows how you used Agile, Apache Spark, and AWS 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; 118 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.
Agile
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.
Apache Spark
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.
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.
Azure
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.
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.
Google Cloud
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
- Compensation: What is the approved compensation range, and what portion is guaranteed base pay rather than variable compensation?
- 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
The headline alone does not tell the whole story for Senior Data Engineer at Valtech. The clearest decision context comes from work-location flexibility has usable detail, the visible application effort can be estimated, and source continuity adds useful context.
Work-location flexibility has usable detail
The work-location pattern is “Highly location-flexible”, with Remote Freedom at 96/100.
The visible application effort can be estimated
Provider-mediated application path. 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
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?
Comparable salary data is not available for this job yet.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
Missing compensation is not treated as negative evidence; this dimension simply remains unavailable.
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
- The available work-location evidence indicates strong location flexibility.
Visible cautions
No specific caution is supported by the currently available evidence. This does not prove that none exists.
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.
- 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.
The observable Remote Freedom evidence is strong and contains few explicit restriction signals.
Still check: 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 observable work-location signals indicate unusually strong location flexibility.
The source posting date is today.
Compensation intelligence
How competitive is this pay?
Comparable salary data is not available for this job yet.
Why this analysis?
- Comparable salary data is not available for this job yet.
- Salary confidence: Not available. No disclosed salary can currently be normalized for comparison. Missing compensation is not treated as low-quality salary evidence.
- Range quality: No disclosed range.
Employer compensation communication
Pay transparency at Valtech
Limited 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
Valtech — 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 (1) · Marketing (1) · Executive (1)
Visible seniority mix: Mid-career (5) · Senior / lead (7) · Leadership (1)
Current employment-type mix: Full time (11) · Contract (1)
Other current roles from this employer
Senior Talent Development Analyst
Remote — Worldwide
Senior / lead · Worldwide remoteSenior Cloud Engineer
Remote — Worldwide
Senior / lead · Worldwide remoteLead Optimizely Solutions Architect
Remote — Worldwide
Senior / lead · Worldwide remotePlatform Database Engineer (MONGO DB)
Remote — Worldwide
Mid-career · Worldwide 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
- What compensation range applies to this role, and which parts of total compensation are fixed versus variable?
- 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?
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
Highly location-flexible
Why this score?
- The source describes the role as open worldwide or globally.
Flexibility signals detected
- Worldwide or global remote eligibility is explicitly indicated.
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?
No obvious trade-off appears in the available job details. That does not mean the role has no drawbacks, so the original listing and employment terms are still worth reading carefully.
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: himalayas.app · 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.
Why Valtech? We’retheexperience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values-driven culture, international careers and the chance to shape the future of experience.
The opportunity
At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries.
We are proud of:
- The work we do and the innovation we drive
- Our values of share, care and dare
- A workplace culture that fosters creativity, diversity and autonomy
- Our borderless, global framework, which enables seamless collaboration
The role
We are looking for an experienced Senior Data Engineer to design, build, and optimize modern, cloud-based data platforms that power analytics, AI, and data products across the organization. Beyond technical delivery, we're looking for someone genuinely curious about the business problems behind the data — someone who can apply common sense thinking, detailed analysis, and experience-based recommendations to help derive and shape business requirements, not just implement them as given.
You will work on scalable batch, streaming, and near-real-time pipelines, enabling high-quality, curated datasets while ensuring robust data governance, security, and observability across the data ecosystem. You will also play a key role in supporting AI and GenAI systems, enabling pipelines for machine learning, causal modeling, and LLM-powered applications such as RAG and agent-based systems.
This role can be delivered on either of our two core cloud ecosystems — AWS (with Databricks) or Azure/Fabric (with Databricks or native Fabric tooling) — and you'll be staffed on projects that match your strongest platform. You don't need experience in both. Additional experience across Snowflake or GCP is considered a strong plus.
You will collaborate closely with data scientists, ML engineers, and platform teams to ensure the data foundation supports production-grade, decision-oriented AI systems.
Role responsibilities
Build & Data Platform Engineering
Design and implement scalable data platforms and pipelines on whichever of our two core ecosystems matches your strengths — AWS (with Databricks) or Azure/Fabric (with Databricks or native Fabric tooling) — with exposure to other environments (Snowflake, GCP) considered a plus. This includes developing reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake, and building ingestion, transformation, and curation workflows for both structured and unstructured data.
You will implement modern data architectures including lakehouse patterns and medallion layering (bronze, silver, gold) within Databricks or Fabric, ensuring systems are reusable, scalable, and aligned with enterprise needs.
Enable AI, GenAI & Data Products
Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems. You will enable data pipelines for GenAI use cases (including LLMs, RAG pipelines, and vector-based data flows), as well as agent-based architectures and intelligent workflows, ensuring that data is model-ready and production-grade — leveraging Databricks' MLflow and Unity Catalog, or the equivalent Azure/Fabric and Azure ML tooling, depending on the platform you work in.
Data Modeling, Orchestration & Automation
Design scalable logical and physical data models for analytical and operational use cases, ensuring consistency across domains. Orchestrate workflows using tools such as Airflow, dbt, Databricks Workflows, Azure Data Factory, or equivalents, with strong focus on automation, reliability, and maintainability of end-to-end pipelines.
Architecture, Governance & Observability
Apply modern architecture patterns including event-driven and streaming architectures, and ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC), using tools such as Unity Catalog and AWS Lake Formation, or Microsoft Purview, depending on the platform in use.
Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence, ensuring systems remain trustworthy and production-ready.
Data Serving, Integration & Optimization
Enable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems, including ML and AI platforms. Continuously monitor and optimize pipelines and infrastructure for performance, scalability, and cost efficiency across our core cloud ecosystems.
Requirements Discovery & Business Partnership
Bring genuine curiosity to every engagement — ask the right questions to understand not just what stakeholders are asking for, but why. Apply common sense thinking, detailed analysis, and experience-based recommendations to help derive and refine business requirements, surfacing gaps or better alternatives where they exist rather than simply executing a brief as written.
Collaboration
Work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions. Support adoption of data products and contribute to best practices across the data and AI ecosystem.
Must have qualifications
Technical skills
Strong hands-on experience with Apache Spark and Delta Lake, and strong programming skills in Python and SQL. Proven experience building batch and streaming data pipelines and production-grade data platforms, with solid understanding of data modeling, data quality, and governance principles.
Cloud & Platforms (Key Requirement) — choose your track:
- Track A: Strong, demonstrable hands-on experience with AWS data services (e.g., S3, Glue, EMR, Redshift, Kinesis, Lambda) and the Databricks Lakehouse Platform
- Track B: Strong, demonstrable hands-on experience with Microsoft Azure / Fabric (e.g., Data Factory, Synapse, Fabric Lakehouse, Azure Databricks)
You only need deep expertise in one track — we treat both as equally weighted core platforms and will align you to projects based on where your experience is strongest. Familiarity with other modern data platforms such as Snowflake or GCP is a plus but not required.
Architecture & Systems Thinking
Experience with lakehouse architectures and distributed data systems, and strong understanding of scalability, reliability, and performance considerations in data pipelines.
Mindset
Naturally curious, with strong problem-solving skills focused on scalability and reliability, and a collaborative approach to working in cross-functional teams. Comfortable applying common sense thinking, detailed analysis, and experience-based recommendations to help derive and challenge business requirements rather than taking them at face value. Experience in Agile or consulting environments is beneficial.
Nice to have qualifications
Deep expertise in both tracks — AWS and Azure/Fabric, across Databricks and native Fabric tooling — is a strong plus, though not required, as is experience with GenAI and AI data systems (e.g., RAG pipelines, vector databases, LLM data preparation), CI/CD for data pipelines, and infrastructure-as-code tools such as Terraform or CloudFormation.
Additional exposure to streaming technologies (e.g., Kafka), Spark optimization, or advanced analytics and ML workloads (including causal or experimentation platforms) is valuable. Experience building data products or large-scale analytics platforms is also beneficial.
Commitment to reaching all kinds of people
We design experiences that work for all kinds of people - and that starts with our own teams. At Valtech, we’re intentional about building an inclusive culture where everyone feels supported to grow, thrive and achieve their goals. No matter your background, you belong here. Explore our Diversity & Inclusion site to see how we’re creating a more equitable Valtech for all.
The benefits
This is a position based in Poland.
Mental and physical health:
- 24 working days of paid vacation
- National holidays covered
- Sick leave (up to 20/year)
- Unpaid leave (up to 20/year)
- Medical insurance
- Multisport card OR Multikafeteria
- Maternity & paternity leave support
Personal and professional development:
- Internal workshops & learning initiatives
- Professional certifications reimbursement
- Participation in professional local & global communities
- Growth Framework to manage expectations and define the steps to move towards the selected career
- Mentoring program with the ability to become a mentor or a mentee to grow to a higher position
Valtech Poland has a system of progressive benefit packages – the longer you stay with the company, the more benefits you get!
Beyond a competitive compensation package, we offer:
- Flexibility, with remote and hybrid work options (country-dependent)
- Career advancement, with international mobility and professional development programs
- Learning and development, with access to cutting-edge tools, training and industry experts
Your application process
Once you apply, our Talent Acquisition team will review your application. Your CV should cover key information on relevant experiences and expertise. We do not require information such as age, gender, marital status, or a headshot in your application. We review all candidates based on skills, experience, and potential.
⚠️ Beware of recruitment fraud!
We are committed to inclusion and accessibility. If you need reasonable accommodations during the interview process, please either indicate it in your application or let your Talent Partner know.
About Valtech
Valtech is the experience innovation company that exists to unlock a better way to experience the world. By blending crafts, categories, and cultures, we help brands unlock new value in an increasingly digital world.
At the intersection of data, AI, creativity, and technology, we drive transformation for leading organizations, including L’Oréal, Mars, Audi, P&G, Volkswagen Dolby, and more.
At Valtech, we don’t just talk about transformation. We make it happen. Our people are the heart of our success, and we foster a workplace where everyone has the support to thrive, grow and innovate.
Are you ready to create what’s next? Join us.
Originally posted on Himalayas
Cross-source evidence
Do the current sources tell the same story?
4 decision facts are corroborated by multiple current sources.
Current sources agree: Worldwide Remote.
Current sources state the same normalized location evidence.
Current sources state the same date.
Current sources agree: Full Time.
The supporting sources currently carry different listing text. This may reflect formatting or revisions, so rely on the explicit fact checks above rather than assuming a contradiction.
This compares only facts currently supplied by the sources supporting this deduplicated opportunity. Missing evidence is not disagreement, and source agreement is not an employer-quality or authenticity rating.
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.
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: Himalayas. Destination: himalayas.app. The application opens in a new tab so you can keep this report open.
Found from 2 sources.