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.
- Support quality engineering across technologies such as Airflow, Spark, Databricks, and Snowflake.
- Drive root-cause analysis and resolution of complex data quality issues.
- Establish metrics and processes to monitor data quality and improve reliability across critical data products.
- Participate in Agile planning, technical discussions, architecture reviews, and cross-functional delivery.
- Manage hiring, performance management, career development, and technical growth of team members.
- Foster a culture of engineering excellence, accountability, collaboration, and continuous improvement.
What's Makes You a Great Fit
- 8+ years of hands-on experience in software testing, data quality engineering, data engineering, or a related technical discipline.
- At least 2 years of experience in technical leadership or engineering management.
- Strong experience validating complex ETL pipelines, data transformations, business logic, and large-scale data systems.
- Strong hands-on proficiency in Python and SQL, including analysis of large or terabyte-scale datasets.
- Solid understanding of data quality engineering, test automation, validation frameworks, and CI/CD practices.
- Practical experience with technologies such as Snowflake, Databricks, Spark, and Airflow.
- Strong analytical and problem-solving skills with the ability to investigate complex data issues and identify root causes.
- Experience implementing quality engineering practices within Agile development environments.
- Proven ability to build, mentor, and manage high-performing engineering teams.
- Strong stakeholder management and communication skills, with the ability to work effectively across Data, Product, Engineering, and Analytics teams.
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- A strong ownership mindset with a passion for building scalable, reliable, and high-quality data systems.