Lead Data Engineer

Job highlights

• Lead mentor a team of Data Engineers - code reviews,design reviews,growth plans. Design and operate ETL / ELT pipelines in Python for batch and streaming workloads. Own data modelling across PostgreSQL / Supabase (OLTP) and Cassandra (wide-column).

Job Summary

Lead the design, build, and operations of a production-grade data platform. Own end-to-end pipelines, set engineering standards, and mentor a team of Data Engineers delivering data for analytics, ML, and product use cases.

Experience: 5-8 years | Location: Bangalore/India | Type: Full-time

Responsibilities

• Lead mentor a team of Data Engineers - code reviews, design reviews, growth plans
• Design and operate ETL/ELT pipelines in Python for batch and streaming workloads
• Own data modelling across PostgreSQL / Supabase (OLTP) and Cassandra (wide-column)
• Build and run orchestration on Apache Airflow - DAG design, SLAs, retries, backfills
• Deploy and operate the platform on AWS (EC2-based, self-managed stack)
• Establish data governance: catalog, lineage, ownership, MDM, data quality, profiling, observability
• Drive engineering excellence - Agile delivery, CI/CD, testing, IaC, code review

Product Mindset

• Own outcomes across the full lifecycle - design, delivery, operations, iteration
• Optimise for scale, reliability, and maintainability with measurable SLAs and cost targets
• Prioritise based on user value (analytics, ML, product teams as customers)
• Ship iteratively; measure adoption, quality, and reliability continuously

Must-Have

• 5-8 years in data engineering with proven experience leading and mentoring engineers
• Expert Python and strong SQL
• Production experience with PostgreSQL, Supabase, and Apache Cassandra
• Hands-on ownership of Apache Airflow at scale
• Comfortable running data workloads on AWS EC2 (Linux, networking, self-managed services)
• Strong ETL, data modelling, orchestration fundamentals
• Practical experience implementing data governance - MDM, data quality, data profiling, observability, lineage/catalog
• Agile, CI/CD, and coding best practices as everyday habits

Good-To-Have

• Data security and compliance - encryption (KMS/at-rest/in-transit), PII handling, GDPR/PDPA
• Streaming (Kafka, Kinesis) and lakehouse (Iceberg, Delta) exposure
• IaC (Terraform, Ansible) for self-managed AWS stacks

Education

Bachelor's or Master's in Computer Science, Engineering, or related field.

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