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,...