LexisNexis
Website:
lexisnexis.com
Job details:
A senior Data QA & automation engineer is needed to direct quality assurance activities for the enterprise data lake platform. This position involves developing thorough testing protocols, possessing substantial knowledge in both general and data quality validation, and utilizing AI tools to enhance engineering effectiveness for the team.
Responsibilities
- Test Automation & Integration: architect and maintain end-to-end testing frameworks to support a variety of engineers working across various services (APIs, UI/UX, data pipelines, serverless applications).
- Testing Protocol Establishment: define and inform best practices on maintaining quality metrics across our data and application platforms.
- Data Quality and Validation: design and implement frameworks to help profile, monitor, proactively detect issues with critical data products.
- AI-leveraged Engineering: leverage LLMs to accelerate tasks like test case generation, document creation, code review, synthetic/test data generation.
Requirements
- 5+ years in quality focused automation engineering, with a strong understanding of how to set a high bar for quality for complex applications and data systems
- Self-starter and ability to drive impact with minimal high-level direction
- Strong programming skills in Python or node.js, and CI/CD tools
- Experience building internal tools, automation scripts, and operational dashboards.
- Solid understanding of microservices, APIs, and backend architecture principles to design and develop effective internal tools and operational workflows.
- Experience with distributed systems and cloud computing (AWS, Azure, GCP)
- Has worked with data-intensive systems (distributed datastores, data warehouses/lakes, big data processing)
Preferred Qualifications
- Experience with HIPAA or other compliance-heavy industries
- Experience with infrastructure as code (Terraform, Cloudformation)
- Background in data and analytics
Technical Skills
- Programming: Python, node.js, SQL
- Data Technologies: Apache Spark, Kafka, Delta Lake, Databricks, Looker
- Cloud Platforms: AWS, Azure, GCP
- Testing Tools: Playwright, pytest, Jest, great expectations or similar tools.
- Automation: Jenkins, Gitlab CI, Github Actions, Apache Airflow
- Monitoring: Datadog, Prometheus, Cloudwatch
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