Description

You will engineer automation-first data quality controls, monitoring, and remediation at scale. You design, script, and operationalize validation pipelines across cloud and on-prem environments.

Responsibilities

• Engineer automated data quality pipelines that detect, diagnose, and remediate data defects using Python and SQL.
• Implement API-driven integrations (REST/GraphQL) with DQ, catalog, ticketing, and orchestration systems, handling OAuth2, pagination, and retries.
• Embed automated controls into ETL/ELT jobs, including pre/post-load validations, row/column-level checks, and SLA/SLO monitoring.
• Build event-driven alerts and webhook workflows to route exceptions to queues like Jira or ServiceNow with full audit metadata.
• Develop KPI dashboards and operations apps using Power BI and Power Apps to visualize coverage, drift, and rule performance.

Required Skills

• Advanced proficiency in Python and SQL for automation, reusable libraries, and validation pipelines.
• Experience with API engineering: designing/consuming REST/GraphQL APIs, handling auth (OAuth2/Bearer), rate limits, and webhooks.
• Proficiency in building solutions with Power BI and Power Apps for operational reporting and workflow automation.
• Experience contributing to CI/CD workflows (GitLab or equivalent) for DQ assets, including automated tests and secrets management.
• Bachelor’s degree and minimum 5 years related experience, or combined 9 years education/work experience.

Preferred Skills

• Experience with automated ETL/ELT validation, data contracts, and schema enforcement.
• Proficiency with DQ/observability platforms (e.g., Informatica Cloud DQ, Monte Carlo, Anomalo, Collibra OwlDQ) and their SDKs/APIs.
• Cloud experience (Azure, Snowflake) including scripting with providers/SDKs, secrets management, and job orchestration.

Key Skills

Education

Any Graduate