Description

Key Responsibilities:
·        Architect and implement robust automated testing frameworks leveraging PySpark and Databricks-native tools for data validation across Raw, Curated, and Mart layers.
·        Design and implement data quality validation frameworks, including checks on accuracy, completeness, and consistency across transformation layers.
·        Create advanced data quality KPIs, integrating them into automated dashboards to track quality trends across layers.
·        Design metadata-driven tests, integrating with CI/CD pipelines, with coverage on all transformation layers.
·        Lead development of QA user stories and acceptance criteria, precisely defining test scenarios for ingestion, transformation, and consumption layers.
·        Perform complex data reconciliation testing across 10+ source systems, ensuring accuracy, completeness, and consistency from source through Mart.
·        Own the end-to-end testing lifecycle (QA, Staging, Production), defining what and when to test at each stage and ensuring sign-off criteria are met.
·        Partner closely with data engineers to troubleshoot pipeline failures, connectivity issues, and performance bottlenecks.
·        Set standards for data lineage and auditability, ensuring every transformation step can be validated and traced.
·        Plan, facilitate, and manage User Acceptance Testing (UAT) involving business users for data visualization tools such as Tableau running on Databricks.
·        Prepare UAT test scenarios aligned with business use cases, guide users through testing, and gather actionable feedback.
·        Drive defect triage, resolution, and retesting, ensuring readiness for production release.
·        Work within a SAFe Agile framework, participating in PI planning, sprint ceremonies, and cross-team coordination. Collaborate with DevOps, Data Engineers, Data Scientists, and

Product Owners to integrate QA into CI/CD pipelines.
·        Provide regular updates to project and senior management on progress of QA milestones and tasks.

Required Skills & Expertise:
·        Minimum of 5+ years of solid experience in Data Engineering with proven experience testing and validating data pipelines in Databricks, including medallion architecture.
·        Proficient in creating testing framework for validating Data Quality.
·        Proficient in Databricks notebook, PySpark, Python, SQL, and data quality testing.
·        Expert with testing AI/BI models, ensuring data quality from feature engineering through model scoring.
·        Experience in CI/CD pipelines (e.g., Azure DevOps) for automated test execution.
·        Strong knowledge of data governance (data lineage, audit trails, compliance testing).
·        Excellent problem-solving skills with the ability to work in a fast-paced environment.
·        Experience with tools such as Azure Purview and Profisee MDM is preferred

Key Skills

Education

Bachelor's degree