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Advanced SW Engineer-Data Engineering -MLOPs

Chennai, India

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Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
Apply at Aptiv

What you’ll work on

Full posting
  • Design and maintain batch and streaming pipelines ingesting vehicle, sensor, and engineering data

  • Build reusable ETL/ELT frameworks producing analytics- and ML-ready datasets

  • Add data validation, quality monitoring, and lineage across the data lifecycle

  • Partner with AI/ML and MLOps teams on datasets, features, and interfaces

  • Build and maintain CI/CD, infrastructure-as-code, and containerised data services on AWS and/or Azure

From the employer’s posting
Design, develop, and maintain scalable batch and streaming pipelines that ingest data from vehicles, sensors, APIs, files, databases, and cloud services.
Build reusable ETL/ELT frameworks for transforming raw data into curated, analytics-ready and ML-ready datasets.
Implement automated data validation, reconciliation, schema checks, lineage, metadata, and quality monitoring across the data lifecycle.
Partner with AI/ML and MLOps teams to deliver high-quality datasets, features, and interfaces for model development, validation, and production workflows.
Build and maintain automated CI/CD pipelines, infrastructure-as-code, containerised workloads, and deployment practices for data services across AWS and/or Azure.
Education & alternatives
Your Background - Bachelor’s or master’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related discipline, or equivalent practical experience. - 5–10 years of professional experience in data engineering, software engineering, or a closely related field.

Tools in this posting

  • C
  • Java
  • Python
  • Scala
  • SQL
  • AWS
  • Azure
  • Databricks
  • dbt
  • Delta Lake
  • Docker
  • Grafana
  • Iceberg
  • Kafka
  • Kibana
  • Kubernetes
  • NoSQL
  • Power BI
  • Spark
  • Terraform
  • Airflow
  • C++
Source — Tool mentions in context
- Experience with ADAS, autonomous driving, automotive, IoT, telemetry, sensor, image, video, or other high-volume engineering datasets. - Working knowledge of Java, Scala, C, or C++ where integration with platform or embedded systems is required. - Experience mentoring engineers or leading the technical delivery of a workstream.
- 5–10 years of professional experience in data engineering, software engineering, or a closely related field. - Strong programming skills in Python and advanced working knowledge of SQL. - Hands-on experience designing and operating production ETL/ELT pipelines for large-scale datasets; experience at petabyte scale is preferred.
- Build reusable ETL/ELT frameworks for transforming raw data into curated, analytics-ready and ML-ready datasets. - Design data models and storage patterns across data lakes, lakehouses, warehouses, and SQL and NoSQL systems. - Develop APIs and data services that enable secure, efficient access to raw and processed data.
- Apply security, privacy, access-control, retention, and governance requirements to data products and platforms. - Build and maintain automated CI/CD pipelines, infrastructure-as-code, containerised workloads, and deployment practices for data services across AWS and/or Azure. - Partner with AI/ML and MLOps teams to deliver high-quality datasets, features, and interfaces for model development, validation, and production workflows.
- Experience with distributed data processing technologies such as Apache Spark or an equivalent managed platform. - Practical experience with at least one public cloud platform (AWS or Azure) and its data, compute, storage, security, and monitoring services. - Experience with workflow orchestration and scheduling tools such as Apache Airflow, Azure Data Factory, AWS Step Functions, or equivalent.
- Practical experience with at least one public cloud platform (AWS or Azure) and its data, compute, storage, security, and monitoring services. - Experience with workflow orchestration and scheduling tools such as Apache Airflow, Azure Data Factory, AWS Step Functions, or equivalent. - Proficiency with Git, automated testing, code review, and CI/CD practices.
- Experience with Docker, Kubernetes, serverless architectures, and infrastructure-as-code tools such as Terraform or CloudFormation. - Exposure to data lakehouse, transformation, or table-format technologies such as Databricks, dbt, Delta Lake, Apache Iceberg, or equivalent. - Experience with monitoring and visualisation tools such as Grafana, Kibana, OpenSearch/ELK, Qlik Sense, or Power BI.
- Experience with streaming and messaging platforms such as Apache Kafka, Kinesis, or Event Hubs. - Experience with Docker, Kubernetes, serverless architectures, and infrastructure-as-code tools such as Terraform or CloudFormation. - Exposure to data lakehouse, transformation, or table-format technologies such as Databricks, dbt, Delta Lake, Apache Iceberg, or equivalent.
- Exposure to data lakehouse, transformation, or table-format technologies such as Databricks, dbt, Delta Lake, Apache Iceberg, or equivalent. - Experience with monitoring and visualisation tools such as Grafana, Kibana, OpenSearch/ELK, Qlik Sense, or Power BI. - Familiarity with MLOps platforms, feature or dataset management, and data workflows for model training and validation.
Bonus / Preferable to have - Experience with streaming and messaging platforms such as Apache Kafka, Kinesis, or Event Hubs. - Experience with Docker, Kubernetes, serverless architectures, and infrastructure-as-code tools such as Terraform or CloudFormation.
- Hands-on experience designing and operating production ETL/ELT pipelines for large-scale datasets; experience at petabyte scale is preferred. - Experience with data modelling, relational databases, NoSQL systems, and cloud object storage. - Experience with distributed data processing technologies such as Apache Spark or an equivalent managed platform.
- Experience with data modelling, relational databases, NoSQL systems, and cloud object storage. - Experience with distributed data processing technologies such as Apache Spark or an equivalent managed platform. - Practical experience with at least one public cloud platform (AWS or Azure) and its data, compute, storage, security, and monitoring services.

Posting record

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Live
First seen by us
Aug 11, 2026 · 78 days ago
Recorded sightings
231
Last seen by us
Oct 29, 2026

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Job description

View original posting ↗

Advanced SW Engineer-Data Engineering -MLOPs


At Aptiv, we shape the future of mobility by developing technologies that make vehicles safer, greener, and more connected. We are building a new Data Engineering team in Chennai to develop the scalable data platforms that support our Advanced Driver Assistance Systems and autonomous driving solutions.

As a Data Engineer, you will design, build, and operate production-grade data pipelines and cloud-native services for large volumes of vehicle, sensor, validation, and engineering data. You will work with global engineering, product, AI/ML, and automotive-domain teams to make trusted data discoverable, reliable, secure, and ready for analytics and machine learning. Depending on your experience, you may own technical workstreams, define engineering standards, and mentor other engineers as the India team grows.

 

Your Roles and Responsibilities:

Core Competencies & Demonstrated Success:

  • Design, develop, and maintain scalable batch and streaming pipelines that ingest data from vehicles, sensors, APIs, files, databases, and cloud services.
  • Build reusable ETL/ELT frameworks for transforming raw data into curated, analytics-ready and ML-ready datasets.
  • Design data models and storage patterns across data lakes, lakehouses, warehouses, and SQL and NoSQL systems.
  • Develop APIs and data services that enable secure, efficient access to raw and processed data.
  • Implement automated data validation, reconciliation, schema checks, lineage, metadata, and quality monitoring across the data lifecycle.
  • Improve pipeline reliability through observability, alerting, retry and recovery patterns, testing, and systematic root-cause analysis.
  • Optimise distributed processing, storage, and query performance while balancing scalability, availability, and cloud cost.
  • Apply security, privacy, access-control, retention, and governance requirements to data products and platforms.
  • Build and maintain automated CI/CD pipelines, infrastructure-as-code, containerised workloads, and deployment practices for data services across AWS and/or Azure.
  • Partner with AI/ML and MLOps teams to deliver high-quality datasets, features, and interfaces for model development, validation, and production workflows.
  • Collaborate with global product, platform, embedded, validation, security, and domain teams to translate requirements into maintainable data solutions.
  • Contribute to architecture reviews, coding standards, technical documentation, peer reviews, and the continuous improvement of engineering practices.

•    

Your Background

  • Bachelor’s or master’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related discipline, or equivalent practical experience.
  • 5–10 years of professional experience in data engineering, software engineering, or a closely related field.
  • Strong programming skills in Python and advanced working knowledge of SQL.
  • Hands-on experience designing and operating production ETL/ELT pipelines for large-scale datasets; experience at petabyte scale is preferred.
  • Experience with data modelling, relational databases, NoSQL systems, and cloud object storage.
  • Experience with distributed data processing technologies such as Apache Spark or an equivalent managed platform.
  • Practical experience with at least one public cloud platform (AWS or Azure) and its data, compute, storage, security, and monitoring services.
  • Experience with workflow orchestration and scheduling tools such as Apache Airflow, Azure Data Factory, AWS Step Functions, or equivalent.
  • Proficiency with Git, automated testing, code review, and CI/CD practices.
  • Good understanding of data quality, schema evolution, idempotency, partitioning, performance tuning, observability, and failure recovery.
  • Ability to troubleshoot complex data and platform issues using a structured, evidence-driven approach.
  • Strong communication and collaboration skills, with experience working across functions and geographies.

Bonus / Preferable to have

  • Experience with streaming and messaging platforms such as Apache Kafka, Kinesis, or Event Hubs.
  • Experience with Docker, Kubernetes, serverless architectures, and infrastructure-as-code tools such as Terraform or CloudFormation.
  • Exposure to data lakehouse, transformation, or table-format technologies such as Databricks, dbt, Delta Lake, Apache Iceberg, or equivalent.
  • Experience with monitoring and visualisation tools such as Grafana, Kibana, OpenSearch/ELK, Qlik Sense, or Power BI.
  • Familiarity with MLOps platforms, feature or dataset management, and data workflows for model training and validation.
  • Experience with ADAS, autonomous driving, automotive, IoT, telemetry, sensor, image, video, or other high-volume engineering datasets.
  • Working knowledge of Java, Scala, C, or C++ where integration with platform or embedded systems is required.
  • Experience mentoring engineers or leading the technical delivery of a workstream.

 

Why join us?

You grow at Aptiv.  

Aptiv's winning culture is global by design. We bring together diverse perspectives, cultures, and business contexts to solve our customers' toughest challenges. In this environment, every individual can grow, lead, and make an impact, regardless of background, because inclusion fuels how we innovate and win.  

You make an impact at Aptiv.  

One of the strengths of humanity is our drive to progress, to improve, to achieve more tomorrow than we did yesterday. People need solutions they can trust when it matters most. At Aptiv, you are building those solutions every day. 

You have support at Aptiv.  

We ensure you have the resources and support you need to take care of your family, your physical health, and your mental health with a competitive benefits package. 

 

 

Your Benefits at Aptiv:

  • Higher Education Opportunities (UDACITY, UDEMY, COURSERA are available for your continuous growth and development);
  • Life  and accident insurance;
  • Sodexo cards for food and beverages
  • Well Being Program that includes regular workshops and networking events;
  • EAP Employee Assistance;
  • Access to fitness clubs (T&C apply);
  • Creche facility for working parents;

 

Meet the Next-Generation Aptiv:

 

At Aptiv, we deliver innovative solutions that people trust when it matters most. 

Progress is never static. It is about reaching further, exploring new possibilities, and enabling mobility. Aptiv empowers customers to create solutions people rely on—advancing safety and autonomy in Automotive and Commercial Vehicles, enabling mission-critical performance in Aerospace & Defense, and powering intelligent systems across Energy, Enterprise, and Telecommunications. We also drive precision and efficiency in Industrials & Robotics and support life-critical innovation in Medical technologies.  

Guided by our winning culture and values to Think & Act Like Owners, as One Team, and Play to Win, we turn commitment into results and achieve more together.  

 

 Join Us to Deliver Innovative Solutions That People Trust! 

 

Apply today, and together let’s change tomorrow! 


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Privacy Notice - Active Candidates: https://www.aptiv.com/privacy-notice-active-candidates

Aptiv is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity, sexual orientation, disability status, protected veteran status or any other characteristic protected by law.

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