Senior Data Quality Engineer

Job Description

• Define and implement data quality rules (completeness, accuracy, consistency, timeliness)
• Develop and manage data contracts (schema, required fields, formats, constraints)
• Embed data quality checks directly into pipelines using SQL and PySpark
• Perform source-to-target validation during onboarding and migration
• Identify, analyze, and resolve data quality issues across data layers
• Build reusable data validation components and frameworks
• Create data quality metrics, reports, and monitoring
• Work closely with data engineers to ensure DQ is part of pipeline design, not post-processing
• Support governance initiatives including metadata, tagging, and lineage
• Pay rate between 50-65/hr We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day.

We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

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https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements

• Strong SQL skills (joins, aggregations, window functions)
• Basic working knowledge of PySpark / Databricks
• Experience with data validation, profiling, and reconciliation
• Understanding of ETL/ELT pipelines and data flow
• Ability to debug data issues across:
• source → ingestion → transformation → output
• Experience with data quality frameworks (Great Expectations, Deequ, or similar)
• Exposure to data governance tools (Alation, Informatica, DataHub or similar) The right person will be and/or will have:
• Someone who can write and implement data quality logic, not just define it
• Thinks in data contracts and validation frameworks
• Comfortable reading and contributing to pipeline code
• Strong debugging mindset and attention to detail
• Able to work across engineering and business teams
• Understanding of metadata-driven or contract-based pipelines
• Experience with file-based ingestion and incremental data loads