Sr. Software Quality Automation Engineer
integrichain1 · Pune, MH, in ·
- Seniority
- Senior
- Category
- Industrial engineering
- Experience
- 3+ years
integrichain1 · Pune, MH, in ·
● 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 knowledge of pipeline orchestration and transformation tooling (Airflow, dbt, AWS Glue, Step Functions, or equivalent) and of common ingestion formats (CSV, fixed-width, JSON, Parquet; EDI/X12 an advantage)
● Python data-handling skills applied to testing — pandas, PySpark, or equivalent — for building comparison, profiling, and reconciliation utilities at scale
● Exposure to data quality frameworks such as Great Expectations, dbt tests, or Soda, or experience building equivalent in-house capability
● 3+ years of test automation across UI and API layers — creating, executing, and maintaining automated tests with Selenium WebDriver (v3+)/Playwright, with programming experience in Python (MUST) — complementing, not replacing, the core data testing focus
● Deep BDD expertise — framework design, Gherkin authoring, step library architecture, living documentation
● Proven JMeter experience — test plan design, distributed execution, and results analysis
● Hands-on experience working in an AI-augmented or agentic development/testing environment (e.g. Claude Code, Copilot, AWS Bedrock, or equivalent)
● Experienced working with Agile methodologies, such as Scrum, Kanban
● Strong analytical and problem-solving skills with a track record of owning data quality outcomes end-to-end — accountable to team-level quality outcomes, not just individual deliverables
● Influences technical decisions on the team through well-supported proposals and demonstrated expertise; beginning to shape team practices through example and mentorship
● Excellent written and verbal communication skills within a geographically distributed, cross functional team
● Key competencies required: Problem-Solving, Analytical, Collaboration, and Accountability. Strong communication skills & stakeholder management. Advantage if,
● Has Healthcare/Life Sciences domain experience — particularly pharma commercial data (distributor 867/852 files, chargebacks, claims, gross-to-net, or master data management)
● Has experience designing and enhancing test automation frameworks, especially data validation frameworks built in-house
● Team leading experience Professional Approach
● Ready to work in flexible working hours and collaborate with US/India/Colombia teams
● Good communication skills (written, verbal, listening, and articulation)
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