Lead Data Engineer, ITC

WHO YOU'LL WORK WITH

This role is part of the Nike's Content Technology team within Consumer Product and Innovation (CP&I) organization, working very closely with the globally distributed Engineering and Product teams. This role will roll up to the Director Software Engineering based out of Nike India Tech Centre.

WHO WE ARE LOOKING FOR

We are looking for experienced Technology focused and hands on Lead Engineer to join our team in Bengaluru, India.

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).

• 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.