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Remote mid-level Data Engineer with popular title and 5-7 years attracts high applicant competition.
Core data engineering skills transferable, but geospatial/time-series and specialized databases increase domain specificity.
Explicit 5-7 years plus many mandatory technologies and domain-specific tooling increases filter strictness.
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Build and manage end-to-end scalable data pipelines for AI-driven analytics involving high-volume sensor, satellite, and third-party data.
Design, optimize, and troubleshoot batch and real-time data workflows using Apache Airflow, Spark, Kafka, and cloud-based architectures.
Ensure data quality, performance, observability, and SLA compliance within a cloud-native environment while collaborating closely with customer teams.
5+ years of experience in Data Engineering and production-scale data platforms.
Strong proficiency with Apache Airflow, Apache Spark, Kafka, Python, and SQL.
Experience with PostgreSQL, TimescaleDB, PostGIS, DuckDB, and cloud-based data lake architectures like AWS S3.
Remote job; comfortable working in Ubuntu/Linux environments.
Experienced in handling geospatial and time-series data at scale in distributed systems.
Demonstrated ability to translate business requirements into data products through stakeholder management and collaboration.
Operational ownership mindset with skills in pipeline optimization, production troubleshooting, and maintaining robust documentation.