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Open nowPosted 46 days ago

Manager - Data Quality Engineering

Workable (global search)109,826 open roles

Where
Bengaluru, KA, India
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Your applicationOpen nowManager - Data Quality EngineeringWorkable (global search) · Bengaluru, KA, India

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  5. 34.0%30 days
This job: posted 46 days ago

Workable (global search) median: 2 days open

The posting

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟮𝟯𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟯𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟮𝟯-𝟯𝟬 𝗟𝗣𝗔)

Experience: 9+ yrs

Location: Bengaluru, Karnataka, India

Job Type: Full-time

We are looking for an experienced Manager – 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.
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