Data Engineering Manager – E2E Data Platform

Data Engineering Manager – E2E Data Platform At Aptiv, we shape the future of mobility by developing technologies that make vehicles safer, greener, and more connected. We are building and scaling a Data Engineering team in Chennai to deliver the trusted, high-performance data platforms that support our Advanced Driver Assistance Systems and autonomous driving solutions. As the Data Engineering Manager, you will build, lead, and develop a high-performing team responsible for large-scale vehicle, sensor, validation, and engineering data platforms. You will combine people leadership with strong technical depth to define the team’s strategy, architecture, delivery roadmap, and operating model. Working across global engineering, product, AI/ML, MLOps, validation, security, and automotive domain teams, you will ensure that trusted data is discoverable, reliable, secure, cost-efficient, and ready for analytics and machine learning. You will be accountable for team capability, predictable execution, production health, stakeholder outcomes, and the continued growth of the Chennai organisation. Your Roles and Responsibilities: Core Competencies & Demonstrated Success: Build, lead, coach, and retain a high-performing Data Engineering team in Chennai, including hiring, onboarding, goal setting, performance management, succession planning, and career development. Define the team’s short- and long-term technical strategy, architecture principles, platform roadmap, and capability plan in alignment with global ADAS, autonomous driving, AI/ML, and product priorities. Own the delivery of scalable batch and streaming platforms that ingest and process vehicle, sensor, API, file, database, and cloud-service data. Establish reusable platform capabilities for ingestion, ETL/ELT, transformation, data modelling, data access, and the creation of analytics-ready and ML-ready datasets. Provide technical direction through architecture reviews, design governance, engineering standards, and pragmatic, 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 service-level objectives, 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.