Quality Assurance Manager

Key Responsibilities:

Manage a QA team of 6 engineers focused on validating data pipelines, APIs, and front-end applications.

Define, implement, and maintain test automation strategies for:

ETL workflows (Airflow DAGs)

API contracts, performance, and data sync

UI automation for internal and external portals

Collaborate with Data Engineering and Product teams to ensure accurate data ingestion from third-party systems such as Workday, ADP, Greenhouse, Lever, etc.

Build and maintain robust automated regression suites for API and UI layers using industry-standard tools.

Implement data validation checks, including row-level comparisons, schema evolution testing, null/missing value checks, and referential integrity checks.

Own and evolve CI/CD quality gates and integrate automated tests into GitHub Actions, Jenkins, or equivalent.

Ensure test environments are reproducible, version-controlled, and equipped for parallel test execution.

Mentor QA team members in advanced scripting, debugging, and root-cause analysis practices.

Develop monitoring/alerting frameworks for data freshness, job failures, and drift detection using Airflow and observability tools.

Technical Skills:

Core QA & Automation:

Strong hands-on experience with Selenium, Playwright, or Cypress for UI automation.

Deep expertise in API testing using Postman, REST-assured, Karate, or similar frameworks.

Familiar with contract testing using Pact or similar tools.

Strong understanding of BDD/TDD frameworks (e.g., Pytest-BDD, Cucumber).

ETL / Data Quality:

Experience testing ETL pipelines, preferably using Apache Airflow.

Hands-on experience with SQL and data validation tools such as:

Great Expectations

Custom Python data validators

Understanding of data modeling, schema versioning, and data lineage.

Languages & Scripting:

Strong programming/scripting skills in Python (required), with experience using it for test automation and data validations.

Familiarity with Bash, YAML, and JSON for pipeline/test configurations.

DevOps & CI/CD:

Experience integrating tests into pipelines using tools like GitHub Actions, Jenkins, CircleCI, or GitLab CI.

Familiarity with containerized environments using Docker and possibly Kubernetes.

Monitoring & Observability:

Working knowledge of log aggregation and monitoring tools like Datadog, Grafana, Prometheus, or Splunk.

Experience with Airflow monitoring, job-level metrics, and alerts for test/data failures.

Qualifications

15+ years in QA/QE roles with 3+ years in a leadership or management capacity.

Strong foundation in testing data-centric and distributed systems.

Proven ability to define and evolve automation strategies in agile environments.

Excellent analytical, communication, and organizational skills.

Preferred:

Experience with data graphs, knowledge graphs, or employee graph modeling.

Exposure to cloud platforms (AWS/GCP) and data services (e.g., S3, BigQuery, Redshift).

Familiarity with HR tech domain and integration challenges.