About this role
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 frameworkInvestigate 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 teamManage QA work items in ClickUp throughout the delivery lifecycle — logging failures, tracking resolutions, promoting confirmed bugs, and closing issues when re-tests passCollaborate with Data Engineers to agree expected behaviours, review data contracts, and validate fixes before they reach UAT or productionCoordinate with UAT stakeholders to align acceptance criteria and share QA findings in a way that non-technical audiences can act onProvide client-facing QA assurance — joining delivery meetings to explain our testing approach, walk through results, and answer questions on QA methodology, coverage, and processIdentify gaps and improvements in SPHERE — raise well-specified change requests and feature requests; contribute to the platform codebase where appetite and skill allowKeep QA coverage current as new views and data sources are onboarded — updating baselines, refreshing metadata, and extending test coverage without being askedEngage 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 productionTest failures are investigated quickly, described clearly, and handed to developers with enough evidence that they can reproduce and fix without back-and-forthClickUp boards reflect the current state of QA — no stale or phantom issues, and Dev Issues are closed promptly when tests go greenClients and internal stakeholders feel well-informed and reassured about QA rigour, without needing to ask twiceSPHERE improves over time because gaps in the platform are named, specified, and tracked — not just worked aroundQA 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 correctnessHands-on experience with Databricks — running queries, navigating Unity Catalog, reading Spark job outputs and understanding what they mean for data qualityWorking knowledge of PySpark or Spark SQL — enough to read pipeline code, understand transformations, and trace where data issues originateUnderstanding of Lakehouse / medallion architecture (bronze-silver-gold) and how data flows and changes shape across layersFamiliarity with YAML-based configuration and a willingness to author structured test definitions programmaticallyComfortable with Git and basic engineering practices — branching, committing, reading diffs, and understanding what changed between pipeline versionsExperience 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 focusDemonstrable experience investigating data issues in a complex, multi-source environment and communicating findings clearlyExposure to structured test frameworks, data observability tooling, or formal QA methodology in a data contextExperience working directly with development teams in an agile or iterative delivery environmentClient-facing or stakeholder-facing experience — comfortable presenting technical findings to non-technical audiences