Sr. Software Quality Automation Engineer

● Work alongside AI agents as co-workers within our AI-SDLC framework — using Anthropic Claude Code, GitHub Copilot, and AWS Bedrock throughout design, development, testing, deployment, and maintenance cycles ● Drive work from Jira tickets and Confluence documentation as the source of truth for requirements, decisions, and context ● Leverage deep domain and product knowledge to evaluate and challenge requirements — ensuring they are precise, complete, and structured for effective processing by both human engineers and AI agents ● Participate actively in Agile ceremonies — sprint planning, stand-ups, retrospectives, and backlog refinement — to keep delivery on track and priorities aligned ● Surface and mitigate quality risks before they escalate — proactively communicating test coverage gaps, flagging scope concerns during planning, and helping keep the team's quality commitments on track ● Own the test strategy and automation architecture with data quality as the primary focus — spanning ETL/ELT pipeline validation, source-to-target reconciliation, functional, end-to-end (Playwright), BDD, and performance (JMeter) layers ● Build and maintain robust Python-based test frameworks and BDD feature suites — including reusable SQL and pandas/PySpark assertion libraries for data comparison, profiling, and reconciliation — that align test coverage to biopharma business requirements ● Design and govern JMeter performance test plans covering both application and data layers — pipeline throughput, load-window adherence, and large-volume query response; analyse results and drive remediation with development teams ● Own end-to-end data quality across the platform's ingestion and transformation pipelines — designing and automating validation of raw, staged, and curated layers in Snowflake and PostgreSQL ● Write and own the complex SQL that proves business logic independently of the pipeline code — reconciling source, staging, and curated layers rather than trusting transformation output at face value ● Build automated reconciliation suites covering row counts, control totals, referential integrity, deduplication, late-arriving data, slowly changing dimension handling, and idempotency on pipeline reruns ● Design and maintain reusable test data — synthetic and masked production-like datasets that exercise edge cases, nulls, boundary values, historical restatements, and malformed or out of-spec source files ● Validate orchestration behaviour end-to-end — dependency ordering, retries, partial-load recovery, backfills, incremental vs. full loads, and failure alerting ● Test schema evolution and data contracts between upstream sources and downstream consumers, catching breaking changes before they reach client-facing outputs ● Define, automate, and report on data quality rules and SLAs — completeness, accuracy, timeliness, uniqueness, and conformity — making data quality visible to the team and to stakeholders ● Serve as the go-to QA engineer for the…