Senior Data Engineer

About the role: You will be a core member of the client's data engineering team, working with the Lead Data Engineer. You build and run the data pipelines, shared data models and quality controls that turn logs, events, measurements and telemetry from embedded platforms (TCAM, VCU, Embedded Apps Platform), cloud, test and application sources into trusted datasets for troubleshooting, quality follow-up and platform insights. Responsibilities:

• Build and maintain automated, production-grade data pipelines (batch and streaming) from embedded, cloud, test and application sources
• Process logs, events, measurements and time-series telemetry at high volume
• Develop shared data models, schemas and transformations; build curated, analysis-ready datasets and views
• Implement data quality checks, pipeline monitoring, alerting, data lineage and documentation
• Support dashboards and analytical use cases for troubleshooting, reliability analysis and product quality follow-up
• Provide production support: troubleshoot failures, find root causes, write runbooks
• Write tests; take part in peer reviews of code, data models and data quality
• Follow security, privacy and access-control rules; handle sensitive data correctly
• Document your work and support knowledge transfer and handover to client's engineers
• Work with embedded, cloud, platform and mobile teams; raise risks and dependencies early

Must have:

• 6 to 10 years in data engineering, with at least 4 years hands-on on production data pipelines
• Strong Python and SQL
• Pipeline orchestration (Airflow, Dagster, Azure Data Factory or similar)
• Data modelling (data modeling), transformation and ETL/ELT (Spark/PySpark, dbt or similar)
• Data quality checks, monitoring, alerting and data lineage
• Cloud data solutions on AWS, Azure or GCP
• Logs, event data, telemetry or time-series data; APIs and messaging (REST, Kafka, MQTT)
• Git, CI/CD, unit and data testing
• Troubleshooting and root cause analysis of production data pipelines
• Understanding of security, privacy and sensitive data handling
• Good English; clear documentation

Good to have:

• Automotive, embedded systems, connected car, IoT or mobile analytics experience
• Dashboards and visualization (Grafana, Power BI, Tableau, Kibana)
• Log and monitoring tools (ELK, OpenSearch, Splunk, Prometheus)
• Databricks, Snowflake or BigQuery; Docker, Kubernetes
• Agile delivery (Scrum, Kanban, Jira)

Education: BE, BTech, MCA, MSc or equivalent in Computer Science, Engineering or IT