We're hiring a Data Engineer (7+ years) to design deploy independently, and operate production-scale data platforms in fully air-gapped, secure customer environments - no cloud dependency, with manual deployment and troubleshooting expected. The ideal candidate has hands-on experience with Apache Airflow, Spark, PostgreSQL, Docker, Python, SQL, and S3-compatible storage (MinIO), along with strong knowledge of geospatial and time-series data, and is comfortable owning infrastructure end-to-end without managed cloud services - from data pipelines to deployment to troubleshooting in fully isolated environments.

Requirements

• 7+ 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 within isolated environment.

• 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 productive data ingestion,



transformation, and bulk-loading pipelines without external connectivity

• Containerize and deploy applications using Docker and Docker Compose in air-gapped environments

• Work with S3-compatible object storage (e.g., MinIO) for on-prem data storage

• Proven experience deploying via Docker/Docker Compose in air-gapped or restricted-network environment.

• Comfortable working in Ubuntu-based environments with manual/local infrastructure management

• Strong proficiency in Python, SQL, Airflow, Spark, and PostgreSQL

• Experience with geospatial and time-series data

• Collaborate with customer teams to translate business requirements into data products

• Ensure data quality, lineage, observability, performance, and SLA compliance

• Troubleshoot production issues on-site/remotely without cloud tooling support

• Create and maintain technical documentation, data models, and operationa

📌 Data Engineer (New Delhi)
🏢 spark eighteen
📍 New Delhi