Data QA Engineer / Data Quality Engineer (m/w/d)

Everience

Rotterdam

On-site

EUR 65,000 - 90,000

Full time

14 days+

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Job summary

Everience in Rotterdam seeks an experienced IT Software Quality Engineer to drive QA across complex data platforms and cloud ecosystems, with a focus on ETL and Big Data testing. You will implement data validation, SQL testing, and test automation, collaborating with cross-functional teams to ensure data accuracy and reliable pipelines across Azure Data Factory, Databricks, and Data Lake Gen2.

This role offers opportunities to shape quality standards, manage defects, and contribute to

Qualifications

Bachelor's or Master's Degree in Computer Science, Information Technology, Data Engineering, or related technical discipline.
Experience in ETL testing and data validation across complex data pipelines is required.
Strong SQL and data reconciliation capabilities.
Experience with test planning, execution, and reporting across BI and data platforms.
Familiarity with Azure data services and cloud testing practices.

Responsibilities

Perform end-to-end testing of ETL and Data workflows.
Validate data extraction, transformation, and loading processes.
Conduct data reconciliation and cross-system validation activities.
Ensure data quality, completeness, consistency, and accuracy across source and target systems.
Analyze requirements and create detailed test scenarios and cases.
Develop test strategies and ensure coverage across releases.
Execute functional, integration, system and regression testing.
Maintain automated data validation frameworks using Python, PySpark, and Pandas.
Validate pipelines on Azure Data Factory, Databricks, and Data Lake Gen2.
Perform API testing where applicable and manage defect lifecycle.

Skills

ETL Testing / Big Data Testing

Manual Testing

BI / Reporting Validation

Advanced SQL

Data Validation and Reconciliation

End-to-End Test Lifecycle Management

Azure DevOps

ETL Frameworks and Processes

Education

Bachelor's or Master's Degree in Computer Science / Information Technology / Data Engineering

Tools

Python

PySpark

Pandas

Azure Data Factory (ADF)

Azure Databricks

Azure Data Lake Gen2

Postman

Job description

Everience is an international consulting group delivering AI-augmented digital services and placing people at the heart of the AI revolution.

With a presence in Europe, Africa, Asia and America, Everience offers its 4,000-strong workforce the most demanding and stimulating environment in which to transform and develop their skills, learning about new AI-based roles and building their future employability.

Through its Symbiotic Academy the group offers a unique hub for training, practical application and exchange where everyone can experiment, learn, and progress in the fields of artificial intelligence and data.

In accordance with its core purpose of orchestrating the symbiotic relationship between humans and AI in the workplace, Everience is making the augmented employee the driving force of a “symbiotic age”, where AI enhances talents and opens up new career opportunities.

We are looking for an experienced IT Software Quality Engineer to drive quality assurance activities across complex data platforms and cloud-based ecosystems. The role requires strong expertise in ETL and Big Data testing, data validation, SQL, and test automation. The candidate will be responsible for ensuring data accuracy, integrity, and reliability across end-to-end data pipelines while collaborating with cross-functional teams to deliver high-quality solutions.

Key Responsibilities:

ETL & Data Validation:

Perform end-to-end testing of ETL and Data workflows.
Validate data extraction, transformation, and loading processes.
Conduct data reconciliation and cross-system validation activities.
Ensure data quality, completeness, consistency, and accuracy across source and target systems.

Test Planning & Execution:

Analyze business requirements and create detailed test scenarios and test cases.
Develop comprehensive test strategies aligned with business and technical requirements.
Execute functional, integration, system and regression testing.
Ensure adequate test coverage and quality across releases.

SQL & Data Analysis:

Perform advanced SQL-based data validation and analysis.
Validate complex data transformations, aggregations, joins, and business rules.
Investigate and troubleshoot data-related defects and inconsistencies.

Automation & Tools (good to have):

Develop and maintain automated data validation frameworks using Python, PySpark, and Pandas.
Create reusable automation utilities to improve testing efficiency.
Support continuous improvement of test automation capabilities.

Cloud & Data Platform Testing

Validate data pipelines built on Azure Data Factory (ADF).
Test data processing and storage solutions on Azure Databricks and Azure Data Lake.
Ensure seamless integration between cloud-based data services.

API & Integration Testing

Validate data exchange across integrated applications and services.
Perform API testing using tools such as Postman.

Defect Management

Manage the complete defect lifecycle from identification through closure.
Collaborate closely with development and integration teams to resolve issues effectively.

Reporting & Stakeholder Communication

Track and report testing progress, quality metrics, risks, dependencies, and blockers.
Provide regular status updates to project stakeholders.
Work closely with developers, business analysts, product owners, and business users to ensure successful delivery.

Quality & Risk Management

Identify quality risks proactively and drive mitigation plans.
Ensure compliance with organizational QA standards, processes, and governance practices.