Automation Test Lead – Data & Platform Engineering

• Strong experience with Python (test frameworks, libraries, CLI tools) and advanced SQL
• Hands-on experience with DBT, Airflow, Snowflake, or similar; knowledge of ETL/ELT and data modeling
• Experience using Great Expectations/Soda; understanding of lineage and catalog systems
• Experience testing Kafka/Kinesis and knowledge of delivery semantics
• Familiarity with Git workflows; CI/CD using Jenkins, GitHub Actions
• Experience with AWS and Infrastructure as Code (Terraform, CloudFormation)
• 9+ years in automation/QE; 5+ years building frameworks for data platforms at scale

Nice to have:

• API contract testing (PACT)
• Basic UI automation
• Exposure to data mesh/data products
• Monitoring tools like Prometheus/Grafana
• Experience in regulated domains (healthcare, life sciences, finance)

• Own a scalable, modular, metadata-driven test framework for batch/streaming data pipelines, APIs, backend services, and end-to-end validations
• Enable plug-and-play components, parallel execution, environment isolation, and deterministic runs
• Engineer SQL-based assertions, schema validation, data contracts, lineage, and config-driven test definitions
• Perform destructive testing including schema drift, backward incompatibility, partial failures, and event ordering issues
• Validate ETL/Streaming at scale with reconciliation, incremental/backfill validation, and delivery semantics
• Integrate or extend Great Expectations or Soda for data quality and observability; create custom validations and quality dashboards
• Enforce CI/CD and DataOps practices with pre-merge gates, release blockers, and integration with GitHub Actions and Jenkins
• Manage test data with synthetic generation, masking, anonymization, and edge case simulation
• Conduct performance and reliability testing including benchmarks, concurrency, stress, and cost/time optimization
• Ensure security and compliance covering PII/PHI checks, encryption, audit requirements, and regulatory frameworks (GxP/SOX/ISO)
• Lead cross-functional quality efforts with data engineers, platform teams, and architects
• Conduct root-cause analysis of production data issues and reduce flaky tests