Data Platform Engineering Manager

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, BigQuery, 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, SageMaker, PyTorch, TensorFlow, 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 GitOps 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, DataDog, 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.