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Common mid-level Data Engineer title, 3–6 years range, metro hiring, and broad tech requirements drive high competition.
Core data engineering skills are widely transferable across industries despite some domain-specific data types, so low sensitivity.
Explicit 3–6 years plus mandatory Spark, SQL, cloud, Airflow, and language requirements make filters highly strict.
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Design, build, and operate large-scale batch and streaming data pipelines using Apache Spark and distributed data technologies.
Develop and optimize ETL workflows and efficient SQL queries across multiple database systems including BigQuery, PostgreSQL, Elasticsearch, and cloud-native platforms.
Monitor and troubleshoot production data pipelines to ensure reliability and performance while collaborating with product and engineering teams to deliver production-grade data systems.
3–6 years of experience building distributed data processing systems or large-scale data platforms.
Strong knowledge of Apache Spark, distributed computing concepts, ETL design, HDFS, and SQL.
Proficiency in Java or Python and experience with relational and analytical databases such as PostgreSQL, Elasticsearch, or DuckDB.
Experience with workflow orchestration tools like Airflow and at least one public cloud platform (GCP preferred; AWS or Azure also accepted).
Experienced in building scalable, reliable, and maintainable data engineering solutions with a focus on performance and data quality.
Comfortable working in fast-paced environments with ownership of end-to-end data platform components and collaboration across teams.
Familiar with Linux environments, cloud-native development, and leveraging AI-assisted coding tools to improve engineering productivity.