qa engineer (manual) in data infrastructure
Posted Updated 3
views
Summary
Manual QA engineer for Sagacity's client data platform built on Databricks Lakehouse pipelines. Day to day: author and run YAML-driven test plans, investigate data test failures through the Databricks stack, manage QA work in ClickUp, and give client-facing QA assurance. Core tech: SQL, Databricks/Spark, PySpark, Git.
Описание
Sagacity provides a client data platform based on Databricks Lakehouse pipelines, gold-layer views, and analytics datasets supporting marketing, billing, credit, and debt outcomes across multiple industries.
Задачи
- Author, run, and maintain test plans across client deployment phases using SPHERE's YAML-driven test framework;
- Investigate test failures through the Databricks Lakehouse stack, identify root causes, and provide evidenced findings;
- Manage QA work items in ClickUp throughout the delivery lifecycle;
- Collaborate with Data Engineers to agree expected behaviours, review data contracts, and validate fixes;
- Coordinate with UAT stakeholders on acceptance criteria and QA findings;
- Provide client-facing QA assurance in delivery meetings;
- Identify gaps and improvements in SPHERE and raise change and feature requests;
- Contribute to the platform codebase where appropriate;
- Keep QA coverage current as new views and data sources are onboarded;
- Engage with AI agents for test authoring, investigation, result analysis, and documentation.
Требования
- Strong SQL skills, including window functions, CTEs, and aggregations;
- Hands-on experience with Databricks, Unity Catalog, and Spark job outputs;
- Working knowledge of PySpark or Spark SQL;
- Understanding of Lakehouse and medallion architecture;
- Familiarity with YAML-based configuration and structured test definitions;
- Comfortable with Git and basic engineering practices;
- Experience with AI-assisted workflows and large language model agents;
- 3-5+ Years of experience in data quality, data testing, analytics engineering, or data engineering with a strong quality focus;
- Experience investigating data issues in complex, multi-source environments;
- Experience with structured test frameworks, data observability tooling, or formal QA methodology in a data context;
- Experience working directly with development teams in agile or iterative delivery environments;
- Client-facing or stakeholder-facing experience presenting technical findings to non-technical audiences.
Условия
London, England, United Kingdom.