Data QA Engineer (SQL)

Location: Hybrid, Monument St, London EC3R 8AJ, UK

Salary: Competitive salary

Company

hirify.global delivers data quality and analytics solutions for clients across financial services, retail, energy, telecoms and media, water, and the not-for-profit sector.

What you will do

• Write and run SQL to check, profile, and reconcile data across source systems, staging, data warehouse, and lakehouse layers.
• Test ETL/ELT pipelines end to end, validating transformation logic, business rules, and bronze-to-silver-to-gold data flows.
• Perform data quality checks for completeness, accuracy, duplicates, schema, referential integrity, row volumes, migration parity, and source-to-target matching.
• Create and maintain test plans and cases from mappings, specifications, and acceptance criteria using hirify.global SPHERE’s YAML-driven test framework.
• Investigate anomalies, distinguish pipeline defects from source-data issues, and provide evidence developers can act on.
• Work with clients, UAT teams, Data Engineers, and Platform Engineers; manage QA work in ClickUp and improve SPHERE and related tooling using AI-assisted tools where appropriate.

Requirements

• Strong SQL skills, including joins, CTEs, aggregations, and window functions.
• Experience testing ETL/ELT processes and data warehouse or lakehouse environments.
• Understanding of data quality testing, including completeness, accuracy, duplicates, schema, and transformation logic.
• Experience with source-to-target reconciliation and writing test cases from data mappings or specifications.
• At least 2 years of experience in data testing, data quality, data analysis, analytics engineering, or QA with a strong data focus.
• Experience investigating data issues in complex, multi-source environments and communicating findings clearly.
• Client-facing or consulting experience, including presenting findings to non-technical audiences, managing expectations, and building relationships.
• Experience working with development or data engineering teams in an agile or iterative delivery environment.
• Comfortable using Git, including branching, committing, and reading diffs; detail-oriented, self-directed, organised, adaptable, and collaborative.
• Able to travel throughout the UK and have the right to work in the UK.

Nice to have

• Python, PySpark, or Spark SQL for reading pipeline code and tracing transformations.
• Databricks, including Unity Catalog and medallion architecture.
• Azure or AWS data services such as Data Factory, Synapse, S3, Glue, or Redshift.
• Data testing tools such as dbt tests, Great Expectations, or Soda.
• YAML-based configuration or structured test frameworks.
• Experience using AI agents and LLMs as productivity tools.

Culture & Benefits

• Hands-on, client-facing work involving multiple client streams and collaboration with data and QA teams.
• Expected behaviours include clear communication, constructive challenge, early escalation of issues, knowledge sharing, and continuous process improvement.
• Commitment to personal development, especially in data platforms and AI-assisted engineering.
• Hybrid working arrangement in London with travel throughout the UK.