We are looking for a Data Quality Engineer with strong experience in data quality assurance, validation, and monitoring within modern data platforms. Experience with Snowflake is mandatory, along with hands on exposure to data engineering and analytics environments.
Key Responsibilities Design, implement, and maintain data quality checks, rules, and validation frameworks
- Monitor data accuracy, completeness, consistency, freshness, and integrity across pipelines Identify, analyze, and resolve data quality issues in collaboration with data engineering and business teams
- Build automated data quality dashboards, s, and reports
- Ensure compliance with data governance, audit, and regulatory standards
- Perform root cause analysis for data defects and recommend preventive controls Support data migrations, integrations, and enhancements with quality sign offs
Key Skill /Skill Specialization Data Quality Engineering, Snowflake SQL, Data Validation & Profiling, ETL/ELT Pipelines, Data Governance & Analytics.
Desired Skills Experience with data quality tools (Great Expectations/DBT tests), Python or PySpark, cloud platforms (Azure/AWS), and CI/CD for data pipelines.
Skills
- Strong experience in data quality engineering / data validation roles
- Hands on experience with Snowflake (tables, views, SQL optimization, data profiling) Advanced SQL skills for data analysis and validation
- Experience with ETL/ELT pipelines and data warehouses Understanding of data governance, metadata, and lineage
- Experience working with large, complex datasets
- Deep understanding of Data Quality frameworks, data validation strategy, unit testing, integration testing, reconciliation testing, and their appropriate usage
- Experience leading large-scale data profiling initiatives to identify anomalies, completeness issues, data drift, null patterns, duplicates, and business rule violation
- Experience validating source-to-target mappings, crosswalk tables, master/reference data, and reconciliation processes
Preferred / Desired Skills
- Experience with data quality tools (DBT tests, Informatica DQ, Talend DQ, etc.)
- Exposure to Python / PySpark for data validation and automation
- Knowledge of cloud platforms (Azure / AWS / Google Cloud Platform) Familiarity with CI/CD for data pipelines
- Experience in healthcare, finance, or regulated domains (good to have)
Soft Skills
- Strong analytical and problem solving skills
- Excellent communication and stakeholder coordination
- Ability to work independently and in cross functional teams
- High attention to detail and ownership mindset
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