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

 

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.

Create test documentation, coverage reports, data quality dashboards, and release-readiness reports.

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

 

Responsibility:

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