Data QA Engineer

Qualifications

• 2+ years in data testing, data quality, data analysis, analytics engineering or a QA role with a strong data focus
• Investigating data issues in a complex, multi-source environment and communicating findings clearly
• Client-facing or consulting experience: presenting findings to non-technical audiences, managing expectations and building relationships
• Working directly with development or data engineering teams in an agile or iterative delivery environment
• Able to travel throughout the UK and have the right to work in the UK

Required Skills

• Strong SQL: confident writing joins, CTEs, aggregations and window functions to interrogate, profile and reconcile data
• Data pipeline testing: experience testing ETL/ELT processes and data warehouse or lakehouse environments
• Data quality: a solid grasp of how to test completeness, accuracy, duplicates, schema and transformation logic
• Reconciliation and test design: source-to-target reconciliation and writing test cases from data mappings or specs
• Analytical eye: you can tell when the numbers just don’t look right, and work out why
• Engineering basics: comfortable with Git (branching, committing, reading diffs)

Preferred Skills

• Python, PySpark or Spark SQL, enough to read pipeline code and trace transformations
• Databricks, including Unity Catalog and medallion (bronze-silver-gold) architecture
• Azure or AWS data services (e.g. Data Factory, Synapse, S3, Glue, Redshift)
• Data testing tools such as dbt tests, Great Expectations or Soda
• YAML-based configuration or structured test frameworks
• Using AI agents and LLMs as a genuine productivity tool

What Success Looks Like in the Role

• Client data is reconciled and validated end to end before delivery, with no material data quality issues reaching UAT or production
• Numbers that don’t look right are spotted early, investigated thoroughly and explained clearly
• Developers can reproduce and fix defects from your evidence without back-and-forth
• Clients trust our QA and feel well-informed about what has been tested and why, without needing to ask twice
• Test coverage grows with each delivery increment rather than lagging behind
• Our QA tooling and processes improve because gaps are named and fixed, not just worked around

Competencies & Behaviours

• Detail-oriented: you notice when numbers don’t add up and follow the thread until you understand why
• Consultative and confident with clients: you explain findings simply, challenge politely and share bad news early, with a plan
• Self-directed: you don’t wait to be told what to test next; you look at the data and ask the right questions
• Clear communicator: you turn a complex data discrepancy into a finding a developer can act on and a client can understand
• Positive and collaborative: you share knowledge, support peers and improve the process, not just the output
• Organised and adaptable: you manage multiple client streams in parallel and stay calm when priorities shift
• Committed to personal development, especially in data platforms and AI-assisted engineering