Data Quality Automation Engineer (#5100)

About the project:

The Client provides comprehensive operational support and a range of expert services to the world’s leading insurers, brokers, fleet managers, and automotive manufacturers. 3,300 employees across ten countries deliver exceptional standards on a large scale for over 1,200 clients. We help the global insurance market to handle millions of claims each year in the most cost-effective and efficient ways possible.

The Client is embarking on an exciting and challenging transformation program, and our software solutions are a driving force behind this strategy, using cloud computing and leading-edge design patterns.

Key Responsibilities

• Define and implement data quality rules across ingestion, transformation, and reporting layers
• Validate data in Databricks-based pipelines
• Monitor and test Databricks transformations (PySpark/SQL) for correctness and completeness
• Ensure Databricks / Power BI reports reflect accurate and reconciled data
• Set up data validation checks (schema, nulls, duplicates, ranges, referential integrity)
• Identify, log, and track data quality issues with root cause analysis
• Collaborate with data engineers and analysts to fix issues
• Build automated data quality monitoring and alerts

Required Skills

• 4-5+ years of Relevant work experience in data analysis, quality assurance, data governance, or a similar field is highly desirable. 
• Strong knowledge of Databricks / Spark (SQL, PySpark)
• Understanding of ETL/ELT pipelines and data transformations (dbt)
• Experience validating BI/reporting outputs (Power BI preferred)
• SQL proficiency for data validation and reconciliation
• Familiarity with data quality frameworks/tools (e.g., Great Expectations is a plus)

Nice to Have

• Experience with AWS data stack
• Experience with data governance or data catalog tools
• Exposure to CI/CD for data pipelines
• Knowledge of data lineage and observability tools

Success Criteria

• Reduced data defects in pipelines and reports
• Automated data quality checks are in place
• Clear visibility and tracking of data issues