Sr. Software Quality Automation Engineer

Job Description

• 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 harder test challenges — the person the team relies on when data complexity, regulatory risk, or system ambiguity is highest
• Produce clear test strategy documents, defect trend analyses, and quality reports that inform team and stakeholder decisions; communicate quality status and risk effectively to both technical and non-technical audiences
• Incorporate pharma manufacturer client needs and regulatory requirements into test coverage decisions; participate actively in incident responses and quality reviews that have direct client impact
• Apply strong analytical and problem-solving skills to trace data defects back to root cause through the pipeline — distinguishing source data issues from transformation logic defects and orchestration failures — anticipating failure modes and designing coverage for them proactively rather than reactively; evaluate quality trade-offs and drive decisions with appropriate rigour
• Demonstrate full accountability for your own work and the team's quality output — holding yourself answerable to team-level quality outcomes, not just individual test deliverables; proactively managing risks, communicating progress, and owning quality end-to-end
• Lift the overall quality output of the team — through proactive test design collaboration, knowledge sharing, unblocking, and consistently raising the bar on what the team ships
• Stay current with the team's evolving test toolset — including AI-augmented testing practices, Playwright updates, BDD patterns, and biopharma regulatory developments — and actively bring relevant updates into team practices
• Partner effectively across backend developers, UI/UX developers, product analysts, and domain stakeholders — communicating clearly within a geographically distributed team
• Mentor junior QA engineers and champion quality best practices across the team
• Evaluate and integrate AI-powered testing tools into the QA workflow