This is a remote position.

We are seeking a Data Engineer with 5+ years of experience to build and scale data platforms powering AI-driven analytics on high-volume sensor, satellite, and third-party data. The ideal candidate will have expertise in designing and managing scalable batch and real-time data pipelines using Apache Airflow, Apache Spark, Kafka, Python, and SQL. Strong experience with PostgreSQL, TimescaleDB, PostGIS, cloud-based data lake architectures, and geospatial/time-series data is essential. The role involves owning data pipelines end-to-endfrom real-time ingestion to analyticswhile ensuring data quality, performance, observability, and reliability in a cloud-native workplace. Strong stakeholder management, problem-solving, and collaboration skills are key to delivering impactful data products and analytical solutions.
- 5+ years of experience in Data Engineering and production-scale data platforms
- Design and manage Apache Airflow pipelines for high-volume sensor, satellite, and third-party data ingestion




- Build and optimize Apache Spark workloads for batch processing, geospatial analytics, and large-scale aggregations
- Develop and maintain PostgreSQL, TimescaleDB, PostGIS, and DuckDB-based data solutions
- Implement Kafka-based streaming pipelines for real-time data ingestion
- Work with Object Storage (AWS S3 or equivalent) for scalable data lake architecture
- Strong proficiency in Python, SQL, Airflow, Spark, and PostgreSQL
- Experience with geospatial and time-series data
- Comfortable working in Ubuntu-based environments
- Collaborate with customer teams to translate business requirements into data products and analytical solutions
- Ensure data quality, lineage, observability, performance, and SLA compliance
- Troubleshoot production issues and optimize pipeline, database, and query performance
- Create and maintain technical documentation, data models, and run-books
- Strong communication, stakeholder management, and problem-solving

📌 Data Engineer u2013 AI-Driven Data Platforms (New Delhi)
🏢 spark eighteen
📍 New Delhi