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 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

7+ years in software quality engineering, with the majority of that time spent on data-centric testing — ETL/ELT pipelines, data warehouses, and reporting/consumption layers.

Strong expertise in requirement analysis, test data design, test planning, and detailed test case design across automation and manual testing. ● Any experience with AWS/Cloud hosted applications is an added advantage. ● Expert-level SQL (MUST) — able to independently write and tune complex queries against Snowflake, PostgreSQL and MySQL (multi-table joins over large fact/dimension tables, window functions, CTEs, aggregations, set operations) to validate transformation logic without depending on developers ● 4+ years specifically testing data pipelines / ETL-ELT processes end-to-end — source-to-target mapping validation, reconciliation, data quality rule design, and root-cause analysis of data defects ● Hands-on experience with a cloud data warehouse (Snowflake strongly preferred) — comfortable with stages, streams, tasks, time travel, clustering, and reading query profiles for tuning ● Working knowle