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Mid-level metro data engineer role with popular title and experience range increases applicant competition.
Medium: core data engineering transferable, but air-gapped and geospatial expertise is specialized.
Explicit 5-7 years plus mandatory Airflow, Spark, geospatial and on-prem deployment experience increases filtering rigidity.
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Own end-to-end design, deployment, and management of production-scale data pipelines and infrastructure in secure, air-gapped environments without cloud dependencies.
Develop and optimize Apache Airflow workflows, Spark batch processes, and geospatial/time-series data solutions using PostgreSQL, TimescaleDB, PostGIS, and DuckDB.
Containerize and deploy data applications using Docker/Docker Compose on Ubuntu-based systems, ensuring data quality, lineage, observability, and SLA compliance.
5+ years of experience in Data Engineering with production-scale data platforms.
Proficiency in Apache Airflow, Spark, PostgreSQL, Docker, Python, SQL, and S3-compatible storage (e.g., MinIO).
Experience working in secure, air-gapped environments without cloud connectivity.
Location requirement: New Delhi, India (on-site or local infrastructure management).
Demonstrated ability to manage complex data workflows and infrastructure in isolated, secure environments, emphasizing operational independence from cloud services.
Strong technical expertise in handling geospatial and time-series datasets at scale using appropriate databases and processing frameworks.
Experience collaborating with customer teams to translate requirements into robust data products while maintaining high standards for data observability and SLA adherence.