Data Engineering

Job description: Data Engineering (Telematics Platform)

Location: Yelahanka, Bangalore

You Will
We are building one of the largest real-time EV telematics data platforms in India processing millions of events per minute from connected vehicles. We are looking for a Module Lead Data Engineering who can architect and guide the team toward a scalable, cost-efficient, and stable data pipeline built on GCS, Spark, and Flink.

To Be Successful, You Will Be Expected To

• Lead design and development of large-scale data ingestion and processing pipelines using Apache Flink, Spark, and Kafka.

• Own the full data lifecycle from real-time ingestion to offline storage in GCS (Parquet/Delta Lake) and analytics in downstream systems (e.g., Pinot/ClickHouse).

• Optimize streaming and batch jobs for performance, reliability, and cost efficiency.

• Define data partitioning, retention, and compaction strategies for telemetry signals and trip/charging data.

• Work closely with product and backend teams to expose processed data via APIs and internal services.

• Mentor a team of data engineers, enforce code quality, and drive technical reviews and documentation

• Troubleshoot production data issues, and build observability for data latency, completeness, and accuracy.

You must have any or a combination of any of the following skills:

• Strong understanding of streaming system internals and event-time processing.
• Experience with data modeling for time-series and telemetry data.
• Proven ability to stabilize large-scale data pipelines and lead technical direction.
• Excellent communication and mentoring skills
• Scope of Telematics Data

You will handle diverse EV telemetry streams including:

• Vehicle signals: speed, SOC, range, temperature, GPS, ignition, mode, and charging data
• Derived datasets: trips, charging sessions, vehicle health, and usage analytics

Processing goals: near real-time insights for app dashboards, dealer analytics, and OTA decisioning

• Tech Stack
• Languages: Python, Java, Scala
• Core Tools: Apache Flink, Apache Spark, Kafka, GCS, Airflow, Pinot/ClickHouse
• Data Formats: Parquet, Avro, JSON
• Infrastructure: GCP (GCS, Pub/Sub, DataProc, Composer), Kubernetes, GitHub Actions.