Data Engineer Senior

Overview

Lead the technical direction of the data engineering practice while remaining hands-on, building and owning production data pipelines end-to-end and guiding and growing a team of engineers.

What you'll do

• Lead the technical direction of the data engineering team by setting standards, guiding design decisions, and reviewing the team's work for quality and scalability.
• Design, build, and maintain new, scalable data pipelines to onboard and integrate data from a wide variety of source systems.
• Work extensively within the Databricks platform, applying the medallion architecture—bronze, silver, and gold layers—to build and evolve reliable, well-structured pipelines.
• Stay current with new Databricks capabilities, including Unity Catalog, Delta Lake features, Delta Live Tables, Databricks SQL, and workflow/orchestration updates, and apply them to improve pipeline design, governance, and performance.
• Design and build new data models grounded in data modeling fundamentals, including fact and dimension modeling.
• Own and optimize workloads in Google BigQuery, staying current with new BigQuery features, including BQML, materialized views, storage/compute optimizations, and new SQL capabilities, and apply them to improve performance, cost efficiency, and scalability.
• Mentor and coach team members on technical best practices, pipeline design, and code quality.
• Continuously evaluate emerging data engineering tools and technologies across the broader data ecosystem and bring recommendations back to the team.
• Partner with analytics, data science, and business stakeholders to translate requirements into robust, reliable data solutions.

What you'll need

• 7–10 years of experience in data engineering, with a demonstrated track record of building and owning production-grade pipelines end-to-end.
• Proven experience leading a team on technical matters, including architecture decisions, code reviews, and mentorship.
• Strong, hands-on experience with the Databricks platform, including practical, working knowledge of the medallion architecture—bronze, silver, and gold layers—and demonstrated awareness of new and evolving Databricks features.
• Strong, hands-on experience with Google BigQuery, including demonstrated awareness and adoption of new BigQuery features.
• Hands-on proficiency in Python, SQL, and Git version control.
• Solid grounding in data modeling fundamentals, including facts and dimensions and the ability to design new data models from the ground up.
• Demonstrated ability to build pipelines across diverse and evolving source systems.

Nice to have

• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience.
• Cloud certification(s), including Databricks Certified Data Engineer, Google Cloud Professional Data Engineer, or equivalent.
• Experience with orchestration/workflow tools, including Airflow, Databricks Workflows, or dbt.
• Experience with CI/CD for data pipelines, including Git-based deployment and testing frameworks for data.
• Exposure to streaming/near-real-time pipelines, including Kafka, Pub/Sub, or Structured Streaming.
• Experience with data governance, cataloging, and lineage tools, including Unity Catalog or Collibra.
• Experience working in Agile/Scrum delivery environments.
• Prior experience as a technical lead, team lead, or engineering manager, even informally.

Details

• Location: Bengaluru, India.
• The source states that the company's teams have flexibility including hybrid work and adaptable hours.