Data Quality Engineer

Role & responsibilities

Data QA & Business-Logic Validation (Primary 70%)

• Design test strategies and test cases for data pipelines, covering both data movement (completeness, counts, schema) and business logic (calculations, transformations, joins, aggregations, business rules).
• Perform source-to-target reconciliation across source systems, the Databricks lakehouse, and Power BI reports.
• Validate that outputs are correct and non-empty before release, including dependency and sequencing checks across pipelines.
• Build reusable SQL- and Python-based validation scripts and contribute to automated data quality checks in pipelines.
• Document test evidence for every change so that releases can be reviewed and audited.
• Log, triage, and track defects in Azure DevOps through to resolution, and verify fixes.

Business Analysis (30%)

• Gather and document requirements with business stakeholders in Finance, Pricing, and Operations, including acceptance criteria.
• Write clear source-to-target mappings and business rule definitions that engineers can build from.
• Coordinate User Acceptance Testing (UAT) with business users and capture sign-off.

Data Analysis

• Profile data to identify quality issues, anomalies, and gaps before they reach the business.
• Investigate data discrepancies reported by users and explain root causes in plain language.

Preferred candidate profile

• 5-8 years in data QA, ETL / ELT testing, or data-focused business analysis.
• Strong SQL skills for complex validation queries, reconciliation, and data profiling.
• Hands-on experience testing data pipelines on a cloud data platform, preferably Azure Databricks (Databricks SQL, Delta tables).
• Proven ability to validate business logic and calculations, not only record counts.
• Experience writing requirements, source-to-target mappings, and acceptance criteria.
• Experience with Azure DevOps (or Jira) for test cases, defects, and work tracking.
• Experience validating Power BI reports against underlying data.
• Clear communicator who can explain data issues to non-technical business users.