Data Engineering Manager – E2E Data Platform

Lead and grow a high‑performing Data Engineering team in Chennai to build scalable batch and streaming platforms that ingest, process, and model vehicle, sensor, and cloud data for advanced driver assistance and autonomous driving solutions.

• Build, lead, coach, and retain a high‑performing Data Engineering team, including hiring, onboarding, performance management, and career development.
• Define the team’s short‑ and long‑term technical strategy, architecture principles, roadmap, and capability plan aligned with global ADAS and AI/ML priorities.
• Own delivery of scalable batch and streaming platforms for ingesting and processing vehicle, sensor, API, file, database, and cloud‑service data.
• Establish reusable platform capabilities for ingestion, ETL/ELT, transformation, data modelling, and creation of analytics‑ready and ML‑ready datasets.
• Provide technical direction through architecture reviews, design governance, engineering standards, and hands‑on guidance for complex data and distributed‑systems challenges.
• Own platform reliability, data quality, observability, security, privacy, retention, lineage, metadata, and compliance outcomes across the data lifecycle.
• Define SLOs, engineering metrics, operational reviews, incident‑management practices, and continuous‑improvement actions for business‑critical data products.
• Drive performance and cloud‑cost optimisation across compute, storage, data movement, and query workloads while balancing scalability, resilience, and delivery speed.
• Lead quarterly and annual planning, prioritisation, estimation, dependency management, risk mitigation, and predictable execution across multiple workstreams.
• Partner with AI/ML, MLOps, perception, planning, validation, labelling, cloud infrastructure, cybersecurity, product, and programme teams to translate requirements into maintainable platform capabilities.
• Represent the Chennai team in global leadership forums, communicate status and trade‑offs clearly, and influence cross‑functional decisions toward common data‑platform outcomes.
• Foster a culture of ownership, inclusion, engineering excellence, customer focus, innovation, knowledge sharing, and disciplined documentation.

• Leadership experience in ADAS, autonomous driving, automotive, IoT, telemetry, sensor, image, video or high‑volume engineering‑data environments.
• Hands‑on experience with streaming and messaging platforms such as Apache Kafka, Amazon Kinesis, or Azure Event Hubs.
• Proficiency with Docker, Kubernetes, serverless architectures, and infrastructure‑as‑code tools like Terraform or AWS CloudFormation.
• Experience with modern data‑platform technologies such as Databricks, dbt, Delta Lake, Apache Iceberg, or Snowflake.
• Familiarity with MLOps platforms, feature or dataset management, ground‑truth generation, and data workflows for model training, evaluation, validation, and continuous improvement.
• Experience establishing platform product‑management practices, self‑service capabilities, and service‑level objectives.