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Remote, mid-level generalist data engineer role with broad tech requirements increases competition.
Data engineering skills transfer across industries but require platform-specific experience, so moderate sensitivity.
Multiple mandatory technologies and an explicit 4+ years requirement create strict filtering.
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Design, build, and maintain scalable batch and real-time data pipelines using technologies like Kafka, Maxwell, Spark, dbt.
Develop and optimize data models following Medallion Architecture and build cloud-native data platforms using S3, Trino, BigQuery, ensuring scalability and reliability.
Own end-to-end lifecycle of critical data pipelines including monitoring, SLA adherence, and incident resolution, while enabling analytics teams with trusted datasets and dashboards.
4+ years of hands-on experience in scalable data platforms, data lakes, and data warehouses.
Proficiency with Apache Spark (Scala, Python), SQL, and building production-grade batch and streaming data pipelines using Kafka, Maxwell/CDC or similar.
Experience with modern data lake architectures including Medallion Architecture, and cloud-native technologies such as S3, BigQuery, Trino.
Hands-on experience with dbt for building reusable models, automated testing, and documentation; familiarity with CI/CD and modern software engineering best practices.
Experienced in building distributed data systems focused on performance tuning, scalability, and cost efficiency in cloud-native platforms.
Skilled in translating complex business requirements into scalable data architecture and strong stakeholder collaboration across Product, Engineering, and Analytics teams.
Proven ownership of data pipeline lifecycles with emphasis on data quality, observability, lineage, and operational excellence.