Data QA Engineer

You will ensure the data we deliver to our clients is right. Sitting between data analysis and quality assurance, you will test the pipelines, data warehouses and lakehouse platforms that drive marketing, billing, credit and debt outcomes for clients across financial services, retail, energy, telecoms & media, water, and the not-for-profit sector. Working closely with our Data Engineers, Platform Engineer, UAT teams and client stakeholders, you will turn data mappings and specifications into test cases, reconcile data from source to target, investigate anything that doesn’t add up, and explain what you find clearly to both developers and clients. This is a hands-on, client-facing role for someone who enjoys getting stuck into the data. Key Responsibilities 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 how data flows through each layer (bronze silver gold) Carry out data quality checks covering completeness, accuracy, duplicates, schema, referential integrity and row volumes Perform source-to-target reconciliation and migration parity checks, proving counts, totals and key-level data match the specification Write, run and maintain test plans and test cases from data mappings, specifications and acceptance criteria using Sagacity SPHERE’s YAML-driven test framework, keeping coverage current as new data sources are onboarded Investigate failures and anomalies, distinguish pipeline bugs from source data issues, and log clear, evidenced defects developers can act on first time Work directly with clients and UAT teams: explain our testing approach and results, manage expectations on scope, risk and timelines, and build trusted relationships Collaborate with Data Engineers to agree expected behaviour, review data contracts, and validate fixes before they reach UAT or production Manage QA work items in ClickUp, and raise well-specified improvements to SPHERE and our wider QA tooling Use AI-assisted tools to speed up test authoring, investigation and documentation, always checking the output against the data 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 Essential 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) Desirable (bonus points) 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 Experience 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 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 Able to travel throughout the UK and have the right to work in the UK Committed to personal development, especially in data platforms and AI-assisted engineering