QA Engineer

You will own the quality assurance of Sagacity's client data platform deployments — the Databricks Lakehouse pipelines, gold-layer views, and analytics datasets that drive marketing, billing, credit and debt outcomes for our 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 design and execute structured test programmes using SPHERE (Sagacity's internal QA platform), interpret results, triage failures, and provide confident assurance that the data leaving our platform is accurate, complete, and fit for purpose.

A typical day will see you working alongside AI agents for authoring tests, analysing results, and managing work items — treating AI-assisted tooling as a first-class part of your workflow rather than a novelty.

Responsibilities

• Author, run, and maintain test plans across all phases of client deployments — schema validation, referential integrity, row volume checks, data quality rules, and migration parity — using SPHERE's YAML-driven test framework
• Investigate test failures systematically: trace root causes through the Databricks Lakehouse stack (bronze silver gold), distinguish pipeline bugs from data issues, and produce clear, evidenced findings for the development team
• Manage QA work items in ClickUp throughout the delivery lifecycle — logging failures, tracking resolutions, promoting confirmed bugs, and closing issues when re-tests pass
• Collaborate with Data Engineers to agree expected behaviours, review data contracts, and validate fixes before they reach UAT or production
• Coordinate with UAT stakeholders to align acceptance criteria and share QA findings in a way that non-technical audiences can act on
• Provide client-facing QA assurance — joining delivery meetings to explain our testing approach, walk through results, and answer questions on QA methodology, coverage, and process
• Identify gaps and improvements in SPHERE — raise well-specified change requests and feature requests; contribute to the platform codebase where appetite and skill allow
• Keep QA coverage current as new views and data sources are onboarded — updating baselines, refreshing metadata, and extending test coverage without being asked
• Engage with AI agents for test authoring, investigation, result analysis, and documentation — working fluently in an AI-augmented engineering environment

What success looks like in the role

• Client datasets are validated end-to-end before delivery, with no material data quality escapes reaching UAT or production
• Test failures are investigated quickly, described clearly, and handed to developers with enough evidence that they can reproduce and fix without back-and-forth
• ClickUp boards reflect the current state of QA — no stale or phantom issues, and Dev Issues are closed promptly when tests go green
• Clients and internal stakeholders feel well-informed and reassured about QA rigour, without needing to ask twice
• SPHERE improves over time because gaps in the platform are named, specified, and tracked — not just worked around
• QA coverage expands naturally with each deployment increment rather than lagging behind

Competencies & Behaviours

Technical

• Strong SQL skills — comfortable writing and reading complex analytical queries (window functions, CTEs, aggregations) to interrogate data and verify correctness
• Hands-on experience with Databricks — running queries, navigating Unity Catalog, reading Spark job outputs and understanding what they mean for data quality
• Working knowledge of PySpark or Spark SQL — enough to read pipeline code, understand transformations, and trace where data issues originate
• Understanding of Lakehouse / medallion architecture (bronze-silver-gold) and how data flows and changes shape across layers
• Familiarity with YAML-based configuration and a willingness to author structured test definitions programmatically
• Comfortable with Git and basic engineering practices — branching, committing, reading diffs, and understanding what changed between pipeline versions
• Experience with or appetite for AI-assisted workflows — working with large language model agents as a genuine productivity tool, not just for curiosity

Experience

• 3–5+ years in a data quality, data testing, analytics engineering, or data engineering role with a strong quality focus
• Demonstrable experience investigating data issues in a complex, multi-source environment and communicating findings clearly
• Exposure to structured test frameworks, data observability tooling, or formal QA methodology in a data context
• Experience working directly with development teams in an agile or iterative delivery environment
• Client-facing or stakeholder-facing experience — comfortable presenting technical findings to non-technical audiences