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Nike Lead Data Engineer ITC 
India, Karnataka, Bengaluru 
872779540

11.06.2025

WHO YOU’LL WORK WITH

As a Lead Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines and analytics solutions. As a Lead Data Engineer, you will play a key role in ensuring that our data products are robust and capable of supporting our Advanced Analytics and Business Intelligence initiatives.

  • Bachelor’s degree or higher in Computer Science, Engineering, or a related field, or equivalent experience.

  • An experienced data engineer with 6+ years in data engineering and at least 2 years in technical leadership roles.

  • Deep hands-on expertise with Databricks, Snowflake, Spark, Delta Lake, and Apache Airflow (or similar workflow management tools).

  • Expert in SQL and Spark optimization techniques.

  • Strong command of Medallion architecture and data modeling.

  • Experience integrating ML/GenAI pipelines is a plus.

  • Exposure to Tableau or other BI tools.

  • Strong leadership skills with a proven ability to lead and mentor data engineering teams.

  • Excellent problem-solving skills and the ability to design solutions for complex data challenges.

  • Skilled in collaborating cross-functionally and mentoring engineers.

  • Effective communicator, able to work cross-functionally and translate technical concepts for non-technical stakeholders.

  • Preferred: Experience with Kafka/Kinesis or real-time data processing, and certifications in Databricks or Spark (strongly preferred).

WHAT YOU’LL WORK ON

  • Architect and lead the development of scalable data pipelines and platform features.

  • Translate business needs into technical solutions in collaboration with product teams, business stakeholders, and data science teams.

  • Enforce best practices across teams, including governance, quality, and coding standards.

  • Lead code reviews, pair programming, and mentoring.

  • Troubleshoot complex systems and optimize distributed pipelines.

  • Automate deployments using CI/CD and DevOps practices.