Senior Data Quality Engineer, SDET with Databricks and Microsoft Fabric
Summary
Senior data-focused quality engineer (SDET) building automated validation for data pipelines and data products, owning legacy-to-target reconciliation, and wiring quality gates into CI/CD for a UK financial services client migrating to Databricks and Microsoft Fabric. Core stack: Python, SQL, Azure DevOps, SQL Server.
Senior Data Quality Engineer, SDET with Databricks and Microsoft Fabric
18,000 – 21,500 PLN
Client
Our client is a UK financial services group operating in a highly regulated environment, currently rebuilding its data and analytics capability as part of a wider platform modernisation programme.
Project overview
The project migrates data and reporting from a legacy estate onto a modern platform built on Databricks and Microsoft Fabric. Quality engineering is central to the project. The business needs confidence that migrated data reconciles with the source, that pipelines behave as specified, and that reports produce consistent results.
Position overview
This is a data focused quality engineering role rather than an application testing role. You will build automated validation for data pipelines and data products, own reconciliation testing between legacy and target systems, and put quality gates into the delivery pipeline so that a failure blocks a release rather than producing a report that nobody reads.
Responsibilities
Design and implement data testing and quality assurance frameworks.
Build automated validation for data pipelines and data products.
Own source to target reconciliation between legacy systems and the new platform.
Build repeatable regression packs that run unattended.
Write unit tests that execute automatically within CI/CD, with quality gates that can block a release.
Detect and handle schema drift and unexpected structural changes.
Validate reports, dashboards, and semantic models, including measure definitions, filter behaviour, and totals.
Apply AI assisted techniques to test generation and coverage analysis, with human review.
Requirements
5+ years of experience in quality engineering, with meaningful recent experience testing data or ETL processes rather than applications.
Strong Python and SQL skills, used for building test automation rather than only running it.
Experience with pipeline validation, including row counts, control totals, referential integrity, and business rule assertions.
Experience with source to target reconciliation using defined tolerances.
Experience with test automation within CI/CD, including gates that block a release.
Experience with Azure DevOps.
Experience with SQL Server.
English proficiency at a level suitable for direct client conversations.
Nice to have
Experience with Azure Databricks or Microsoft Fabric.
Experience with data quality frameworks, including Great Expectations, dbt tests, Databricks Expectations, DLT quality rules, Soda, or Deequ.
Experience designing a test framework from scratch rather than extending an existing one.
Experience testing semantic models and BI layers.
Experience working in a regulated financial services environment.
Experience with application test automation alongside data testing, including Selenium, C#, SpecFlow, or Playwright.
What We Offer:
Vacation days: Up to 26 business days per year.
10 illness/special days off per year (fully paid, no medical papers needed) for all contract types
Health and life insurance (Luxmed)
MyBenefit platform with Multisport option
Internal psychological support service
English language classes from the first working day
Access to external learning platforms: O’Reilly, LinkedIn Learning, Udemy, and a wide catalog of diverse internal training
Flexible workplace: work from the office, from home, or choose a hybrid option
Opportunities to develop as a public speaker, mentor, or technical interviewer
Fully paid idle (bench) when not involved in a project
Match
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In line with the market for similar positions
Based on 40 similar vacancies
5306$ /month
5306$ /month Your offer
4432 $ 25%
6657 $ median
9314 $ 75%
The range shows the lower and upper market bands: 25% of vacancies pay below the first value, the median is the typical salary, and 75% marks the upper band.