What We Look for in You
A data-focused mindset with a passion for building reliable, scalable, and validated data pipelines A strong understanding of both data engineering and data quality assurance practices A meticulous eye for detail and a proactive attitude toward ensuring end-to-end data integrity The ability to collaborate with diverse stakeholders across engineering, analytics, and business A commitment to delivering clean, secure, and accurate data for decision-making
What You'll Be Doing
Design, develop, and maintain robust ETL/ELT pipelines to process large volumes of structured and unstructured data using Azure Data Factory, PySpark, and SQL-based tools Collaborate with data architects and analysts to understand transformation requirements and implement business rules correctly Develop and execute complex SQL queries to validate, transform, and performance-tune data workflows Perform rigorous data validation including source-to-target mapping (S2T), data profiling, reconciliation, and transformation rule testing Conduct unit, integration, regression, and performance testing for data pipelines and storage layers Automate data quality checks using Python and frameworks like Great Expectations, DBT, or custom-built tools Monitor data pipeline health and implement observability through logging, alerting, and dashboards Integrate testing into CI/CD workflows using tools like Azure DevOps, Jenkins, or GitHub Actions Troubleshoot and resolve data quality issues, schema changes, and pipeline failures Ensure compliance with data privacy, security, and governance policies Maintain thorough documentation for data flows, test logic, and validation processes
What You'll Bring to the Table
Core benefits youll gain

Keyskills: Performance tuning Automation Manager Quality Assurance Performance testing Agile Informatica SSIS JIRA Analytics Python
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