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Lead Data Engineer

Ada Digital Analytics
Posted on

Experience
5 - 10 yrs
Job Location
Bengaluru, India
Vacancy
1
Designation
Lead Data Engineer
Job Type
Not specified

Job Description

# Lead Data Engineer


**Experience:** 58 years | **Location:** Bangalore/India | **Type:** Full-time



ROLE

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.



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

Treat the data platform as a long-lived product, not a project:

  • 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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