Data Platform Engineering Manager

Job Description – Data Platform Engineering Manager Experience: 10+yrs Location: Bengaluru Notice Period: Immediate joiner Role Overview We are seeking an experienced and highly motivated Data Platform Engineering Manager to lead the design, development, scalability, and operations of a modern cloud-native data platform.

This role will drive the architecture and execution of large-scale data processing systems, analytics infrastructure, ML enablement frameworks, and DevOps best practices that power business intelligence, advanced analytics, and rapid product innovation. As a hands-on engineering leader, you will manage and mentor a high-performing team of Data Engineers, DevOps Engineers, and Cloud Platform Engineers while collaborating closely with Product, Engineering, Analytics, and Data Science teams.

Key Responsibilities

Data Platform & Engineering Architect, build, and manage scalable, secure, and high-performance data platforms using technologies such as Apache Hadoop, Hive, Spark, Kafka, Airflow, and Delta Lake. Design and optimize batch and real-time ETL/ELT pipelines to support analytics, reporting, machine learning, and operational use cases.

Develop scalable data models, ingestion frameworks, and streaming workflows for enterprise-scale data processing. Optimize cloud-native data storage and compute solutions using AWS services such as S3, EMR, Glue, Redshift, Athena, and Lambda. Integrate and manage modern data stack tools including dbt, Snowflake, Big. Query, Fivetran, or custom-built connectors.

Establish strong data governance practices including data quality, lineage, cataloging, metadata management, and observability using tools like Apache Atlas, Great Expectations, and Amundsen. Partner with Product, Engineering, Analytics, and Data Science teams to deliver reliable, accurate, and actionable data solutions.

ML & Advanced Analytics Enablement Support AI/ML and Data Science teams by maintaining scalable model training, experimentation, and deployment infrastructure. Build and manage MLOps pipelines and frameworks using MLflow, Sage. Maker, Py. Torch, Tensor. Flow, or similar technologies. Enable model versioning, metadata tracking, automated retraining, and real-time inference workflows.

Ensure scalable and production-ready deployment pipelines for machine learning applications. DevOps & Platform Engineering Lead the implementation of robust CI/CD pipelines, automated testing frameworks, release management, and Git. Ops practices. Implement Infrastructure as Code (IaC) using Terraform, Ansible, or Pulumi.

Manage containerization and orchestration platforms including Docker and Kubernetes (EKS preferred). Own cloud infrastructure management including networking, security, governance, compliance, and cost optimization initiatives. Implement platform monitoring, logging, alerting, and observability using Prometheus, Grafana, ELK Stack, Data.

Dog, or equivalent tools. Drive Site Reliability Engineering (SRE) practices including incident management, root cause analysis, retrospectives, and on-call operations. Leadership & Team Management Lead, mentor, and grow a team of 8–12 Data Engineers, DevOps Engineers, and Platform Engineers. Define team objectives, performance metrics, and engineering best practices.

Foster a culture of ownership, operational excellence, innovation, and continuous learning. Collaborate with cross-functional stakeholders to translate business requirements into scalable and reliable engineering solutions. Drive engineering execution, sprint planning, prioritization, and delivery management. Infrastructure Reliability & Optimization Own platform reliability, scalability, and operational excellence across data and infrastructure systems.