Automation Test Lead – Data & Platform Engineering

• Strong Python skills (test frameworks, libraries, CLI tools) and advanced SQL
• Hands-on experience with DBT, Airflow, Snowflake or similar tools; knowledgeable in ETL/ELT and data modeling
• Familiarity with Great Expectations/Soda and lineage/catalog systems
• Experience testing Kafka/Kinesis delivery semantics
• Proficient with Git workflows and CI/CD tools such as Jenkins and GitHub Actions
• Experience with AWS and Infrastructure as Code tools (Terraform/CloudFormation)

Desired Skills:

• API contract testing (PACT)
• Basic UI automation and exposure to data mesh/data products
• Knowledge of monitoring tools like Prometheus/Grafana
• Experience in regulated domains such as healthcare, life sciences, or finance

Test Automation Architecture & Strategy:

• Own a scalable, modular, metadata-driven framework covering data pipelines (batch/streaming), APIs/backend services, and end-to-end data product validation
• Enable plug-and-play components, parallel execution, environment isolation, deterministic runs

Data Testing Framework Engineering:

• SQL-based assertions/reconciliation, schema validation, data contracts, lineage/freshness validation
• Config-driven test definitions (YAML/JSON)

Destructive Testing:

• Schema drift, backward incompatibility, late-arriving data, partial failures, duplicate/missing/out-of-order events
• Stress, concurrency, retries, DLQ handling, backpressure

ETL/Streaming Validation at Scale:

• Row/aggregate/hash-based reconciliation, incremental/backfill validation
• Delivery semantics validation, window/time-based correctness

Data Quality & Observability:

• Integrate/extend Great Expectations or Soda; custom validations for accuracy, completeness, uniqueness, timeliness
• Quality dashboards

CI/CD & DataOps Enforcement:

• Pre-merge gates, release blockers, selective/parallel test execution
• GitHub Actions/Jenkins integration

Test Data Management:

• Synthetic data generation, masking/anonymization, deterministic datasets, edge case simulation

Performance & Reliability Testing:

• Pipeline/query benchmarks, concurrency/stress testing, data skew analysis, cost/time optimization

Security & Compliance:

• PII/PHI exposure checks, encryption/access control, retention, audit requirements
• Support GxP/SOX/ISO frameworks

Cross-Functional Quality Leadership:

• Work with data engineers, platform teams, architects
• Mentor engineers

Incident Analysis & Prevention:

• Root-cause analysis of production data issues, reduce flaky tests