How to hire a senior data engineer in Europe in 2026 — a practical guide

Q: How do I run a useful technical interview for a data engineer?

Four rounds: async portfolio review, SQL practical with realistic schema (60 min), pipeline system design with specific scenario (60 min), cost-and-operational discussion (30 min). Plus two reference calls. Skip generic behavioural rounds.

May 25, 2026 by Andrii ryzhenko

DATA ENGINEERING · EUROPEAN HIRING · 2026

Author byline: Written by Andrew Ryzhenko, founder of Hiretop. Eight years placing senior engineers into European product teams.

Last updated: May 2026 · Reading time: 22 minutes

Last month a CTO at a Series-B fintech in Amsterdam emailed me at 11pm on a Tuesday. They'd just lost their second data engineer in six months. The first one left for a tier-1 US tech company. The second one, hired three weeks earlier through a marketplace platform, was billing 35 hours a week against a part-time arrangement and producing dashboards that read more like spreadsheets than infrastructure. The pipeline they'd built had a single point of failure that took the whole reporting stack down for nine hours the previous Friday.

"How do we actually hire someone good for this?"

I'm in this conversation more often than I'd like. Data engineering is the role that European founders most consistently get wrong on their first two attempts. Not because the candidates are bad — there's serious senior talent in Berlin, Amsterdam, Paris, Warsaw, Stockholm, Lisbon. Because the role itself is poorly understood, the interview process most companies run misses what actually matters, and the hiring models that work for product engineers often don't work for data engineers.

This is what I've learned across 247 engineering placements at Hiretop, including roughly 30 data engineering roles specifically, in the last four years.

What a data engineer actually does (and what they don't)

The most common mis-hire I see comes from confusing four adjacent roles. They show up in the same job descriptions, the same salary bands, sometimes the same LinkedIn profiles. They are not interchangeable.

Data engineer. Builds the pipelines that move data from operational systems into a warehouse or lake, transforms it into consumable shapes, and runs the infrastructure that keeps the whole thing reliable. Owns ETL/ELT, schema design, data quality monitoring, pipeline performance, warehouse cost optimisation. Lives in SQL, Python, Airflow (or dbt), Snowflake/BigQuery/Databricks, Terraform.

Analytics engineer. Sits between data engineering and analytics. Owns the transformation layer specifically — turning raw warehouse tables into business-readable models. dbt is their primary tool. Less infrastructure, more semantic modelling.

Data scientist. Builds statistical and machine-learning models. Cares about feature engineering, model selection, evaluation metrics. Often a poor fit for infrastructure work; gets bored within six weeks if you ask them to maintain pipelines.

ML engineer. Productionises models. Owns training pipelines, model serving, monitoring, feature stores. Overlaps with data engineering on infrastructure but the centre of gravity is model lifecycle, not data movement.

If you're hiring "a data person" and you don't know which of these four you actually need, your interview process will produce candidates who pass for the wrong reasons. The CTO in Amsterdam I mentioned at the start had hired a data scientist for what was actually a data engineering role. Six months in, the data scientist was unhappy and unproductive, and the team still didn't have working pipelines.

Specific signal: if the work involves moving data from point A to point B reliably, at scale, and keeping it clean, you need a data engineer. If the work involves building models on top of clean data, you need a data scientist or ML engineer. If the work involves transforming clean data into business-readable shapes, you need an analytics engineer.

When you actually need to hire one

Three signals.

You're running production analytics on tools that weren't built for it. Postgres views feeding a Metabase dashboard that takes two minutes to load. Spreadsheets that get manually exported every Monday. A backend engineer who builds queries for the CEO on Slack request. These are pre-data-engineer artefacts. Past 10-15 engineering team members, they break.

Pipeline failures are becoming a known operational risk. When the answer to "why is yesterday's revenue number wrong" is "the pipeline failed silently sometime last week", you have a real data engineering problem. Anyone can build a pipeline. Building one that fails loudly, monitors itself, and recovers gracefully is engineering.

You have data, but you can't easily ask questions of it. This usually means the warehouse exists but the modelling is wrong. Sometimes that's analytics-engineering territory (dbt). Sometimes it's data-engineering (the warehouse architecture itself is broken). Either way, it's a role.

What's NOT a signal: "we should have a data team because Series B companies have data teams". I've watched seed-stage founders hire a data engineer because the round closed and they wanted to look mature. Two of those engagements ended in mutual relief within four months — the engineer was bored, the company didn't have enough data to engineer yet, and the budget was better spent elsewhere.

What "senior" actually means in data engineering

The label is unevenly applied across the European market.

In the US, "senior" typically means 5-8 years of relevant experience with at least one major production-scale system. In Berlin and Amsterdam, the senior title often kicks in earlier (3-5 years) and the band stretches up to staff-level work. In Warsaw and Lisbon, "senior" can mean anything from 4 years to 12, depending on the company.

What matters more than years:

Production scale experience. Has the candidate built or owned pipelines that move at least hundreds of millions of rows a month? At low scale, lots of approaches work. At higher scale, decisions about partitioning, file formats, compute engines, and warehouse choice become load-bearing.

End-to-end ownership. Can they take a vague business requirement, design the data model, build the pipeline, set up the monitoring, write the documentation, and explain it to a non-technical stakeholder? Most "senior" data engineers in the European market are strong on two or three of these. The genuinely senior ones are strong on all five.

Modern stack fluency. Snowflake, Databricks, or BigQuery on the warehouse side. dbt for transformations. Airflow, Dagster, or Prefect for orchestration. Terraform for infrastructure-as-code. Python and SQL go without saying. If a candidate has only worked with one warehouse and one orchestrator, they'll struggle to adapt to your stack. The senior label should imply familiarity with at least two of each.

Cost awareness. A senior data engineer thinks about warehouse spend as a first-class concern. Junior engineers write queries that work. Senior engineers write queries that work and don't make your Snowflake bill jump 40 percent month-on-month. The cost conversation surfaces specific awareness — partition pruning, query result caching, materialisation strategies, clustering keys.

If a candidate can't fluently discuss at least two of those four (production scale, end-to-end ownership, modern stack fluency, cost awareness), they're not senior. They might be a strong mid-level engineer with potential. They are not what you're hiring for.

The European market in 2026

Demand exceeds supply, but the gap varies by city.

Berlin: Saturated for product engineers, tighter for data engineers. Senior data engineering market salaries (gross, full-time, fully loaded with employer contributions): €95,000–€135,000/year. The high end goes to engineers with FAANG or unicorn experience. The middle of the band is realistic for a strong senior with 6-8 years of relevant experience.

Amsterdam: Slightly higher than Berlin due to the 30% ruling for foreign hires. Senior data engineer €90,000–€140,000/year gross; net take-home with ruling can be 35-40% higher than headline.

Paris: €80,000–€110,000/year gross, with employer load adding roughly 45 percent on top. Cadre status applies at senior level in most engagements. The French market is thinner for English-speaking data engineering specifically; expect 2-3 months to hire well.

Stockholm: €85,000–€115,000/year gross at typical Swedish employers. Global tech companies (Spotify, Klarna, Stripe, Databricks) pay €120,000-160,000+ for senior data engineers in Stockholm — these candidates aren't usually moveable on direct hire unless your offer matches the global tech band.

Warsaw / Kraków / Wrocław: €55,000–€80,000/year gross. Strongest market in Central Europe for data engineering specifically — significant local talent, mature consulting market built around Polish dev shops doing analytics work for German and UK clients, strong English. If you're hiring through an embedded agency model, this is where most senior data engineers in the EU price band actually land.

Lisbon / Porto: €60,000–€85,000/year gross. Growing data engineering market. Often used by US companies as a European base for data teams.

Madrid / Barcelona: €60,000–€85,000/year gross. Mid-band similar to Iberia. Senior pool smaller than Poland for data engineering specifically.

Add employer contributions on top: Germany ~22 percent, Netherlands ~19 percent, France ~45 percent, Sweden ~31 percent flat uncapped, Poland ~22 percent, Spain ~30 percent. The all-in cost of a senior data engineer hired direct in Berlin runs €115,000–€165,000/year.

These numbers are 2026 mid-market. Your specific role might price higher (Snowflake architect with 10+ years, ML platform experience) or lower (pure pipeline operator, basic dbt work). Use as a starting point, calibrate with two or three live offers.

Four hiring models, normalised

Same framework I use for product engineering hires, applied to data engineering specifically.

Model 1: Direct hire through your own entity. Cost: market salary + employer load + recruitment fee (15-25 percent of annual gross, more for retained search). Time to hire: 60-120 days for senior data engineers including notice periods. Best for: long-term roles where data engineering is core to product, you have HR/recruiting capacity, and you can absorb the 3-month wait. Common pitfall: the search drags. The Berlin market for senior data engineers had a 5-month median time-to-fill in late 2025; expect similar in 2026.

Model 2: Employer of Record (Deel, Remote, Velocity Global). Cost: market salary + employer load + EOR PEPM ($400-700/month per employee) + FX spread. Time to hire: 2-4 weeks plus engineer's notice period. Best for: you've identified a specific person you want to hire in a country where you don't have an entity. Common pitfall: stock options. Most EORs handle equity awkwardly across jurisdictions, and for senior data engineers (where equity is part of competitive compensation) this becomes a real constraint.

Model 3: Embedded agency on flat monthly rate. Cost: €5,500-7,500/month for a senior data engineer at most EU embedded agencies (including Hiretop). Annualised: €66,000-90,000/year. Time to hire: 5-10 business days for common stacks (Snowflake, dbt, Airflow). Best for: 6-month+ engagements where you want senior capacity without the recruiting overhead. Common pitfall: agencies vary widely in vetting quality; ask for specific recent placements and reference calls before signing.

Model 4: Hourly marketplace (Toptal, Lemon.io, Arc). Cost: $80-150/hour for senior data engineers, $200-250/hour for specialist ML/platform work. Annualised at $110/hour × 160 hours/month: ~$211,000/year per engineer, plus deposit and subscription fees. Time to hire: 24-72 hours. Best for: defined-scope project work under 6 weeks. Common pitfall: paying marketplace premiums for what should be a full-time embedded role. I wrote the full math on this elsewhere — the short version is that the hourly model gets economically painful past month three.

For most European founders hiring a senior data engineer for an ongoing role, the choice is between Model 1 (direct hire) and Model 3 (embedded agency). The decision usually comes down to (a) whether you have 3 months for the search and the operational capacity to run it, and (b) whether you want the engineer engaged through your entity or through the agency's. Past month six the labour-classification risk we'll discuss below pushes more European companies toward Model 3 than five years ago.

How to interview a senior data engineer

Most interview processes I see for data engineering roles are recycled product-engineering interviews. LeetCode problems, system design rounds focused on web service architecture, behavioural questions about team conflict. These work badly for this niche.

Here's the framework that actually surfaces the senior signal.

Round 1: Async portfolio review (30 minutes of candidate's time, 30 minutes of yours). Ask for three specific examples from past work — ideally with anonymised diagrams or code samples — of pipelines they built. For each, you want to understand: what was the data shape and volume; what choices did they make around orchestrator, warehouse, transformation layer; what went wrong and how did they fix it. Senior candidates have stories. Mid-level candidates describe tools they used. Junior candidates describe what they were told to do.

Round 2: SQL practical (60 minutes). Give the candidate a realistic warehouse schema (5-8 tables, 50-200M rows simulated) and three queries to write. Mix easy (single-table aggregations), medium (window functions, two-table joins), and hard (cohort retention with date arithmetic, or detecting duplicates across keys). Senior candidates write working queries in 30-40 minutes. Mid-level candidates need 50-60 minutes and might miss edge cases. Watch how they handle ambiguity — do they ask clarifying questions, or do they make assumptions silently?

Round 3: Pipeline system design (60 minutes). Pose a specific scenario: "We need to ingest 50 million events per day from Segment, transform them in dbt, and serve a marketing-attribution model. Walk me through the architecture." Senior candidates think out loud about partitioning, idempotency, schema evolution, monitoring, cost, and trade-offs between batch and streaming. Mid-level candidates draw a box diagram with arrows. Junior candidates name tools without explaining the trade-offs.

Round 4: Cost and operational thinking (30 minutes). "Your Snowflake bill jumped 40 percent last month. How do you investigate?" This is the most discriminating question I know for senior data engineers. Strong candidates immediately reach for query history, warehouse utilisation, materialisation costs, partitioning strategies. Weaker candidates say "I'd check with the team." There's a real signal here that doesn't show up in any other interview round.

Round 5: Reference calls (30 minutes each, 2 references). Ask: "What's something that surprised you about working with this person, positive or negative?" Vague answers ("they were great") tell you the referee isn't engaging. Specific answers ("they spotted a pipeline bug nobody else had noticed by reviewing dbt manifests in CI") tell you the candidate is real. Bonus question I always ask: "If you were hiring for a different role tomorrow, would you call this person first?" The hesitation in the answer matters as much as the answer itself.

Total candidate time: roughly 3-4 hours across all rounds. Total interviewer time: 4-5 hours. If a candidate doesn't survive that filter, they're not your senior data engineer.

A practical note on takehome assignments. I used to be a fan. I'm not anymore. The reason is that genuinely senior data engineers don't have time to do five-hour unpaid takehomes, and they self-select out of your funnel. The candidates who do take-homes most willingly are usually mid-level engineers with more time than work, or candidates between roles. If you must use one, cap it at 90 minutes and pay for the time. The SQL-practical and pipeline-system-design rounds above surface most of the same signal in a synchronous format.

What I look for that most interviews miss

Three signals that don't show up in standard rubrics.

Comfort with vagueness. Real data engineering work involves taking "we need to track customer engagement" and turning it into a schema, a pipeline, and a model. Junior engineers want a spec; senior engineers turn requirements into specs themselves. Test for this by leaving room in your prompts for the candidate to either clarify or assume.

Documentation instinct. Pipelines that aren't documented become unmaintainable within months. Ask a candidate to walk you through how they document something. Senior engineers describe specific habits: README files in repos, runbooks for failures, schema documentation in dbt, lineage diagrams. Junior candidates say "we use Confluence."

Healthy paranoia. Data engineers who've been bitten by production failures have specific paranoias — about schema drift, late-arriving data, timezone bugs, daylight savings transitions, integer overflow on counter columns. Listen for this. Calm engineers without battle scars usually have less senior experience than the title implies.

A real engagement, anonymised

A Berlin-based health-tech client we work with hired a senior data engineer through Hiretop in March 2025. Context: they were running their entire reporting stack on Postgres views feeding Metabase, daily exports that broke when schema changed, and a part-time data scientist who was unhappy maintaining pipelines. Series B closing in Q3, they needed to demonstrate data maturity for the diligence.

We placed a senior data engineer based in Wrocław. Seven years of experience, including three years at a payments company you'd recognise, fluent in Snowflake, dbt, and Airflow. €6,000 a month flat. Started April 14, 2025. Through to today (May 2026), fourteen months in.

What he built in the first 90 days: migrated the Postgres-views reporting layer to Snowflake, set up a properly partitioned warehouse, replaced ad-hoc daily exports with idempotent dbt models, instrumented monitoring on every critical pipeline. By month 4, the client's CFO was running her own queries in Metabase against the Snowflake-backed semantic layer; by month 6, the data scientist was actually doing data science again instead of fixing broken exports.

Total cost to the client across 14 months: €6,000 × 14 = €84,000. Equivalent on Toptal pricing for a senior data engineer (estimated $130/hour for someone with this profile × 160 hours × 14 months): roughly $291,200 (~€269,600). Difference: about €185,000 across the engagement.

The engineer's still on the team. The client closed the Series B in November 2025 with reporting infrastructure that, in their CTO's words on the diligence call, "looked like we'd had a data team for three years."

That last sentence is the kind of thing that makes Series B diligence go smoothly. Data infrastructure that didn't trip up the technical reference call. Hard to quantify on the balance sheet; very real in valuation conversations.

Three pitfalls I've watched companies walk into

Pitfall 1: Hiring an analytics engineer for a data engineering role. Analytics engineers are amazing at modelling, but most haven't built warehouse infrastructure or production pipelines. If you have a Snowflake account and need someone to write dbt models, hire an analytics engineer. If you need someone to build the Snowflake account itself, choose the warehouse, set up the orchestrator, and design the schema architecture, you need a data engineer. Different role, different interview process.

Pitfall 2: Buying a marketplace freelancer for an embedded role. This is the same trap I see across product engineering, just sharper for data engineering because the work is so context-dependent. A Toptal senior data engineer is excellent for a defined-scope migration. They're a bad fit for an embedded full-time role where the engagement runs 12+ months and the engineer needs to be deep in your stack. Continuity risk plus the annual cost math (~$211k/year via Toptal vs €60-90k via embedded agency) breaks the model past month three.

Pitfall 3: Underestimating labour classification in Germany, Netherlands, France. A long-running freelance data engineer who works full-time hours, uses your equipment, attends your daily standup, and reports to your manager looks indistinguishable from an employee to the tax authority. Reclassification triggers retroactive social contributions (employer-plus-employee share for up to four years in Germany), and in serious cases criminal liability for the managing director. The contract structure matters more than the headline rate. For embedded roles past 6 months, a B2B service contract through an agency holds up under audit; a direct freelance arrangement frequently doesn't.

I wrote the country-specific picture for European buyers in a separate piece on Toptal alternatives for European companies. The labour-classification picture applies to any embedded engineering role, but data engineering is particularly exposed because the work tends to be long-running and integrated with the rest of your engineering operations.

A decision framework for the next thirty days

If you're starting a data engineering hire now, here's the sequence I'd run.

Week 1: Define what role you actually need. Write a one-page document answering: what data volumes and shapes are we dealing with, what tools is the engineer expected to know, what does success look like at month 3, month 6, month 12. If you can't answer these clearly, pause the search and clarify first.

Week 2: Decide on the hiring model. Direct hire if you have 3+ months and operational capacity. Embedded agency if you want senior capacity within 2 weeks and don't want to manage the recruitment process. EOR if you've already identified a specific person in a country where you don't have an entity. Marketplace if the work is bounded under 6 weeks. Most European founders end up choosing between direct hire and embedded agency.

Week 3: Start the search. Direct hire: brief 2-3 retained search agencies with the document from Week 1. Embedded agency: kick off a 15-minute call with the agency to describe the role; expect shortlist in 2-5 business days. EOR: identify the specific candidate first, then engage the EOR.

Week 4: Interview using the framework above. Don't run a generic product-engineering interview process. Use the four-round structure tuned for data engineering specifically.

Months 2-3: Onboarding. First 30 days the engineer should be reading code and shipping small fixes. Days 30-60 they should own one substantial piece of infrastructure. Days 60-90 they should be making architectural decisions independently. If they're still ramping at day 90, you have either a hiring mismatch or an onboarding problem; identify which and respond.

Stack-specific notes for 2026

Some stack-specific patterns I've watched matter more in the last twelve months than they did before.

Snowflake vs Databricks vs BigQuery. All three are mature warehouses in 2026. The choice mostly comes down to your data team's existing skills and the rest of your cloud stack. Snowflake skills are most portable across European candidates — almost every senior data engineer in our network has serious Snowflake hours. Databricks is gaining ground for ML-heavy use cases. BigQuery still wins for GCP-native shops, especially if you're heavy on streaming. If you're choosing the warehouse now, prioritise based on team skills first, integration second, price third — the cost difference between the three has narrowed to roughly 15-25 percent for typical workloads.

dbt as table stakes. Five years ago, dbt was a differentiator. In 2026, every senior data engineer I interview is fluent. If a candidate hasn't worked with dbt, they're either not senior, or they've come from a very specific stack (one of the larger banks, government work, certain enterprise Microsoft shops). That's not necessarily a deal-breaker, but assume a 4-6 week ramp before they're shipping production dbt models at the level of someone already fluent.

Airflow's slow decline. Airflow is still the most common orchestrator in production, but Dagster and Prefect are gaining real ground for new builds. If you're starting fresh, evaluate Dagster — better software engineering primitives, cleaner asset-based mental model. If you're inheriting Airflow, fine; don't migrate just for migration's sake.

Iceberg, Delta, Hudi. Open table formats matter more than they did even a year ago. Senior candidates should be able to discuss the trade-offs: ACID guarantees, schema evolution, time travel, compute-engine flexibility. If a candidate has no opinion here, they've been working at a single warehouse stack for too long.

Streaming. Most companies don't need streaming. They need batch processed quickly. If you think you need streaming, talk to two senior engineers first about whether batch-every-15-minutes would actually solve the problem. Real streaming is operationally expensive and adds a category of complexity (idempotency, backpressure, late-arriving data) that you don't want without good reason.

Observability stack. Pipeline monitoring used to be ad-hoc. In 2026, expect candidates to discuss Monte Carlo, Anomalo, Bigeye, DataHub, OpenLineage, or one of the dbt-native observability tools. They don't have to know your specific stack, but they should have opinions about why monitoring data quality at the pipeline level matters more than dashboard alerts.

What I'd do if I were buying right now

If I were a European founder hiring a senior data engineer for a long-term embedded role tomorrow, I'd:

Skip the marketplace platforms unless the engagement is defined-scope and under six weeks. The annual cost math doesn't work for embedded roles, and continuity risk is high.
Brief two or three embedded agencies on the role and ask for shortlists within five business days. Compare candidates on actual technical depth, not on agency marketing.
Run the four-round interview structure above. Skip behavioural rounds focused on team conflict — they don't discriminate well at this level.
Ask each candidate to walk through a specific pipeline they built recently. Listen for paranoia about schema drift, late-arriving data, cost.
Pay reference calls more attention than the candidate's CV. Two strong references beats five years of stated experience.

The most expensive mistake I see is hiring someone who looks senior on paper but hasn't owned production pipelines. The second most expensive mistake is hiring through a model that doesn't match the engagement shape — marketplace for embedded roles, direct hire for short projects. The third is hiring an analytics engineer or data scientist for what's actually a data engineering job.

Get those three right and the rest of the hire mostly works itself out.

If you're hiring a senior data engineer right now and want to think through the role or the math with someone outside your own team, book a fifteen-minute call. I'll either help you scope the role correctly, point you to a better agency for your specific need, or — if it's a fit — show you who we've placed into similar roles recently. No sales pitch. Just the math.

Frequently asked

What's the difference between a data engineer and an analytics engineer?

A data engineer builds the warehouse infrastructure and the pipelines that fill it. An analytics engineer transforms warehouse data into business-readable models, typically using dbt. Most companies need both eventually; the order matters — you can't run analytics engineering without a warehouse and pipelines first.

How much does a senior data engineer cost in Europe in 2026?

Direct hire: €95-135k gross in Berlin, €90-140k in Amsterdam, €80-110k in Paris, €85-115k in Stockholm, €55-80k in Poland. Add employer load (19-45 percent depending on country) for fully-loaded cost. Embedded agency model: €5,500-7,500 per month flat, €66-90k annualised. Marketplace: $80-150/hour for senior, annualising to ~$211k/year at full-time hours.

How long does it take to hire a senior data engineer in Europe?

Direct hire through your own entity: typically 60-120 days including notice periods. Embedded agency: 5-10 business days for common stacks (Snowflake, dbt, Airflow), 10-20 days for less common ones (Databricks unity catalog specialists, specific cloud-native patterns). EOR: 2-4 weeks plus engineer's notice period at their current employer.

What technical skills should I screen for?

For most embedded roles in 2026: SQL fluency, Python, at least one orchestrator (Airflow, Dagster, Prefect, or dbt jobs), at least one warehouse (Snowflake, BigQuery, Databricks), and infrastructure-as-code (Terraform). Bonus depth on a streaming framework (Kafka, Kinesis) and modern observability tooling. Less common but valuable: data contract tooling, lineage tools (DataHub, OpenLineage), and cost-monitoring habits.

How do I avoid hiring the wrong type of data person?

Write a one-page document describing the work before you start hiring. If the work is mostly transformation of clean data into business-readable models, you need an analytics engineer. If it's mostly building infrastructure that moves and stores data, you need a data engineer. If it's mostly building statistical or ML models on top of clean data, you need a data scientist. The wrong hire in this category costs months, not weeks.

What labour-classification risks should I know about for Germany / Netherlands / France?

A long-running freelance engineer who behaves like an employee can be reclassified by the local tax authority, triggering retroactive social contributions for up to four years (in Germany) and similar exposure in Netherlands, France, Spain, Italy. For engagements past six months, the safer structures are direct employment (via your own entity or an EOR) or B2B service contract through an agency where the engineer is engaged by the agency, not directly by you. Detail in our European Toptal alternative breakdown.

How do I run a useful technical interview for a data engineer?

Four rounds: async portfolio review, SQL practical (60 min, realistic schema), pipeline system design (60 min, specific scenario), cost-and-operational discussion (30 min, "your Snowflake bill jumped 40 percent — investigate"). Plus two reference calls. Skip generic behavioural rounds; they don't discriminate at the senior level.

When you're ready to hire

If you're hiring a senior data engineer for a European product team in 2026 and want a shortlist within five business days, book a fifteen-minute kick-off call. We'll learn the stack, the scope, the engagement length, and within two business days you'll have three to five vetted senior candidates with verified Snowflake, Databricks, or BigQuery experience, real dbt fluency, and references you can call.

If you're not ready yet, see how Hiretop works or check pricing in detail.

Andrew Ryzhenko has been running senior-engineering placements across Europe since 2022 at Hiretop. The benchmarks above come from Hiretop's internal data across 247 engineering engagements (including 30+ data engineering placements specifically), customer-reported salary data, and the 2026 OECD employer-contribution rates.