Hire Senior Data Engineers in Europe
Engineers who build data pipelines you can trust - dbt models that don't silently drift, warehouse schemas that hold up under exec scrutiny, analytics infrastructure that stays fast as it grows. Pipeline reliability is the product.
Engineers who actually fit the craft.
Every data engineers we place has been screened for the specific skills that matter in this discipline - not generic "software engineering" experience repackaged.
Modern data stack fluent
dbt, Airflow / Dagster / Prefect, Snowflake / BigQuery / Databricks. Real production experience - not just course-completion certificates.
Schema-first discipline
Understands dimensional modelling, slowly-changing dimensions, and why your warehouse turns into spaghetti when no one owns the schema. Opinionated without being pedantic.
Data-quality aware
Tests, freshness checks, contract validation. They treat broken pipelines like engineers treat broken builds - you don't ship over red.
Cost-literate
Warehouse costs can spiral fast. Our senior data engineers read cluster usage and query costs with the same attention a DevOps engineer reads the AWS bill.
Engineers in our network. Real people. Long-term.
Every engineer Talzy places is a full-time, locally-employed team member - working exclusively for one company. Not a marketplace, not a rotation.
Every modern data stack, represented in the network.
Sourcing is stack-aware - the shortlist you see only includes engineers with production experience in the technology you specify.
Transformation
Orchestration
Warehouses
Ingestion
Streaming
Quality & obs
The moments a dedicated data actually earns their seat.
First dedicated data hire
Analytics has been a side-hustle for a full-stack engineer. Dashboards contradict each other, definitions are inconsistent, and no one trusts the numbers anymore. A senior data engineer can set up a proper warehouse + dbt project and restore trust within a quarter.
Modern data stack migration
You are moving from 'SQL views in Postgres' or an old BI tool to a modern stack (Snowflake / BigQuery + dbt + BI tool). You need someone who has done this migration before - including the politics of deprecating old dashboards without enraging finance.
Real-time / streaming work
Batch pipelines are not enough anymore. You need event-driven data - Kafka or Kinesis feeding transforms and dashboards in near-real-time. This is a specialised subset of data engineering; ask us upfront and we will confirm bench depth.
Data governance & quality
Leadership wants data contracts, freshness SLAs, lineage, PII masking - the adult version of a data platform. Our senior data engineers with governance experience can stand up the controls without drowning the team in process.
One brief, one shortlist, one data engineer on your team.
Tell us what you need. We come back in 3–5 business days with 3–5 data engineers who fit your stack, your seniority bar, and your team rhythm - already vetted, already interested.
Calculate your data cost.
Salary at-cost (no markup) + a tiered monthly management fee + a workspace fee. No recruitment fee. All shown in USD, per month and per year. Move the controls, see exactly what you will pay.
Typical monthly cost by seniority
All-in, including employment, workspace, and Talzy fee. Ranges cover our three active markets.
Builds and maintains pipelines on a defined scope
Owns the data platform, sets modelling standards
Drives data strategy, governance, and team standards
The bar a data clears before you meet them.
Technical skill is table stakes - alignment, stability, and communication matter just as much. We screen for all four before anyone lands on your shortlist.
Pipeline walkthrough
A real pipeline they have owned in production. Freshness, failure handling, rerun strategy. No resume-level answers pass.
Data modelling exercise
Given a scenario, design the schema - dimensional, facts, grain. We care about tradeoffs, not whether they name Kimball.
Warehouse reasoning
Why would you pick Snowflake over BigQuery? When would you use Databricks instead? Real senior-level tradeoff thinking.
Data-quality scenario
A pipeline that works, but the numbers look off. What would you check, in what order? Reveals how they debug upstream problems.
Async communication check
Writing sample - a Slack incident report on a broken pipeline. Clear + blameless + actionable, or not.
From brief to engineer-at-desk in 2–3 weeks.
Role definition
We lock in requirements, seniority, stack, team fit, and the non-obvious things (timezone overlap, oncall, tooling).
Curated shortlist
Sourced from our active talent network across Latvia, Lithuania, and Poland. Every candidate vetted by a Talzy engineer first.
Your interviews
You run the final technical rounds. We prep candidates on your stack and handle the scheduling friction.
Employ & onboard
Local contract, payroll, and equipment ready. Engineer joins your sprint cycle on day one.
Talzy vs the other options.
A side-by-side honest comparison against the common ways to hire a data engineer - marketplace contractors, in-house recruiting, and outsourcing agencies.
| Talzy | Toptal / Arc | In-house | Agency | |
|---|---|---|---|---|
| Time to first hire | 2–3 weeks | 3–6 weeks | 3–5 months | 4–8 weeks |
| Cost structure | Salary + flat fee | Hourly markup 50–100% | Fully loaded salary | 60–120% markup |
| Employment | Full-time employee | Contractor | Direct employee | Vendor staff |
| You own the relationship | Yes | Yes | Yes | No |
| Long-term retention support | Yes - career program | No | Your HR | No |
| Replacement if it fails | Included | Case by case | You re-recruit | Depends on contract |
Our dashboards had been wrong for six months and no one could figure out why. The senior data engineer Talzy placed found the root cause in her first week - a broken dbt test - and rebuilt the whole modelling layer in the next eight. We can trust the numbers again.
Hiring data engineers through Talzy
Ready to hire your next data engineer?
Tell us the role and team context. We will send a shortlist of matching data engineers from our network within 3–5 business days.