๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐ ๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฎ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฎ๐ฏ-๐ฏ๐ฌ ๐๐ฃ๐) Experience: 9+ yrs Location: Bengaluru, Karnataka, India Job Type: Full-time We are looking for an experienced Manager โ Data Quality E
About the role
๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฎ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฎ๐ฏ-๐ฏ๐ฌ ๐๐ฃ๐)
Experience: 9+ yrs
Location: Bengaluru, Karnataka, India
Job Type: Full-time
We are looking for an experienced Manager โ Data Quality Engineering to lead a team responsible for ensuring the accuracy, completeness, reliability, and integrity of data across large-scale data platforms and pipelines.
This role combines technical leadership, data quality engineering, test automation, and people management . You will work closely with Data Engineering, Analytics, Product, and other technology teams to establish robust validation strategies for complex ETL pipelines, data transformations, and business rules.
The ideal candidate brings strong hands-on expertise in Python, SQL, data quality automation, ETL validation, Snowflake, Databricks, Spark, and CI/CD , along with proven experience leading and developing engineering teams.
Requirements
Key Responsibilities
Lead and mentor a team of Data Quality Engineers supporting multiple data pipelines, platforms, and business domains.
Define and execute data quality and automation strategies for complex ETL pipelines, transformations, and business rules .
Develop scalable approaches for validating data across platforms such as Snowflake and Databricks .
Drive the design, implementation, and continuous improvement of test automation frameworks and validation infrastructure .
Partner with Data Engineers, Analysts, Product Managers, and other stakeholders to ensure data accuracy, completeness, consistency, and integrity.
Analyze large-scale datasets to identify data anomalies, transformation errors, and business logic issues .
Establish engineering standards and best practices for data quality, testing, automation, and validation.
Integrate data quality testing into CI/CD pipelines to enable reliable and continuous validation. โฆ
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