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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.
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