Senior Data Engineer- MLOPS
Chennai, India
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and maintain batch and streaming pipelines ingesting vehicle and sensor data
Build reusable ETL/ELT frameworks for analytics- and ML-ready datasets
Partner with AI/ML and MLOps teams on datasets, features and model workflows
Automate data validation, quality monitoring and pipeline observability
Build CI/CD and infrastructure-as-code for 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.
Partner with AI/ML and MLOps teams to deliver high-quality datasets, features, and interfaces for model development, validation, and production workflows.
Implement automated data validation, reconciliation, schema checks, lineage, metadata, and quality monitoring across the data lifecycle.
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.
Job Location: Chennai Mandatory Skills: Data Engineering in to ADAS, Python Scripting with 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.
- 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.
Good 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
Report an error- Status in our records
- Live
- First seen by us
- Sep 28, 2026 · 26 days ago
- Recorded sightings
- 90
- Last seen by us
- Oct 25, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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Job description
Aptiv -Help shape the future of mobility.
Imagine a world with zero vehicle accidents, zero vehicle emissions, and wireless vehicle connectivity all around us. Every day, we move closer to making that world a reality. Aptiv’s passionate team of engineers and developers creates advanced safety systems, high-performance electrification solutions and data connectivity solutions so that automakers can bring advanced capabilities to more people around the globe. This is how we enable sustainable mobility and help to prevent accidents caused by human error.
We’re hiring an Electrical/Electronics Engineer to design and develop our automotive products in the areas of Active Safety and User-Experience. This position is located at TCI Bengaluru.
Your Role
Experience: 5-10 Yrs
Job Location: Chennai
Mandatory Skills: Data Engineering in to ADAS, Python Scripting with 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.
- 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.
Good 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 can grow at Aptiv. Whether you are working towards a promotion, stepping into leadership, considering a lateral career move, or simply expanding your network – you can do it here. Aptiv provides an inclusive work environment where all individuals can grow and develop, regardless of gender, ethnicity or beliefs.
- You can have an impact. Safety is a core Aptiv value; we want a safer world for us and our children, one with: Zero fatalities, Zero injuries, Zero accidents.
- You have support. Our team is our most valuable asset. We ensure you have the resources and support you need to take care of your family and your physical and mental health with a competitive health insurance package.
Apply today and together let’s change tomorrow!
“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”
https://www.aptiv.com/privacy-notice-active-candidates
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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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Chennai, India
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