Automation QA

Ensures consistency in testing practices and assures quality standards software products by leading the creation of test case documentation and execution within the team. Leads the creation, execution, and documentation of test cases that include: pre / post conditions, test execution steps, and expected results for releases and defects Uses test case results to track project status, forecast completion / budget information, and plan for releases Performs functional system and regression testing and writes SQL / PL SQL to analyze data Recommends design improvements and defect corrections throughout the development process Participates in root cause analysis Provides estimates for planning, development and execution of test efforts across teams and products Develops, enhances and maintains test automation frameworks Independently investigates, diagnoses and resolves product inconsistencies and defects and proposes product improvements Provides input and raises concerns about product functionality during architecture/design sessions at a feature level May define and create automation scripts May identify processes and products where additional automation should be implemented Keeps management informed of technical trends and / or emerging technology Provides technical and leadership mentoring to others in the immediate group Meets training requirements and follows established procedures and proposes new procedures Improves procedures and standards when the opportunity arises Adheres to architecture / design standards

Key Responsibilities Develop automated test suites for Databricks jobs, Delta tables, views, and data transformations. Validate metric calculations, business rules, aggregations, and derived values in Databricks. Test ETL pipelines that ingest, transform, enrich, and hydrate data into Stardog. Perform source-to-target reconciliation across source systems, Databricks, and Stardog. Validate data completeness, accuracy, consistency, timeliness, and referential integrity. Test ontology structures, relationships, classes, properties, and constraints in Stardog. Validate named graphs, virtual graphs, materialized graphs, SPARQL queries, and graph-based data retrieval. Verify data lineage, provenance, mappings, and domain-specific graph relationships. Create automated tests for incremental loads, full loads, updates, deletes, retries, and recovery scenarios. Validate data quality rules and exception-handling processes. Develop test data, validation queries, reusable utilities, and reconciliation frameworks. Integrate data automation tests into CI/CD pipelines. Perform functional, integration, regression, performance, and end-to-end data testing. Analyze failures, document defects, and collaborate with data engineers and platform teams to resolve issues., Guide effective use of agentic IDEs for complex, multi-module or cross-service changes Establish review practices and quality checks for AI-generated code Mentor team members on balancing autonomy, correctness, and maintainability in AI-assisted development Design system architectures that support AI-augmented and agentic development workflows Define guardrails, standards, and governance for the use of autonomous coding agents Evaluate impact of agentic IDEs on SDLC, CI/CD pipelines, security pos

Create test documentation, coverage reports, data quality dashboards, and release-readiness reports. Strong experience in data QA and automation testing. Hands-on experience with Databricks, Spark, Delta Lake, and SQL. Experience testing ETL and data integration pipelines. Experience with Stardog, knowledge graphs, ontologies, SPARQL, or similar graph technologies. Experience validating data across multiple platforms and systems. Experience with Python-based automation frameworks such as Pytest. Experience integrating automated tests with Azure DevOps, GitHub Actions, or similar CI/CD tools. Understanding of data quality, reconciliation, lineage, and validation practices. Experience with Azure cloud services and ADLS. Experience with Kafka or event-driven data pipelines. Experience with ontology-based data modeling. Experience with performance and scalability testing for large data volumes. Experience working in Agile and DevOps environments. Proven experience architecting and delivering systems using agentic IDEs

Ability to: Define architectural intent that agents can follow Break features into agent executable tasks Govern AI autonomy (guardrails, permissions, reviews) Integrate agentic workflows into CI/CD pipelines Experience supervising AI agents across: Multi service systems Legacy modernization Large codebases / monorepos

Strong understanding of: Security implications of autonomous code execution Compliance, auditability, and traceability AI assisted SDLC operating models Core, * Engineering Degree BE/ME/BTech/MTech/BSc/MSc.

• Technical certification in multiple technologies is desirable.