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Mid-level, popular Data Engineer role with broad skills and metro location increases applicant competition.
Core data engineering skills (Spark, ETL, SQL, cloud) are highly transferable across industries.
Explicit 3–6 years plus mandatory Spark, SQL, Airflow and cloud requirements create strict shortlisting filters.
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Design, build, and operate scalable batch and streaming data pipelines using Apache Spark and distributed data technologies.
Develop and optimize ETL workflows, SQL queries, and data models ensuring reliability, performance, and data quality across multiple storage systems including BigQuery, PostgreSQL, and Elasticsearch.
Maintain workflow orchestration (e.g., Airflow) and monitor production data pipelines to ensure operational stability and performance.
3–6 years of experience building distributed data processing systems or large-scale data platforms.
Strong knowledge of Apache Spark, distributed computing, ETL design, HDFS, and SQL.
Proficiency in Java or Python along with experience using relational and analytical databases (e.g., PostgreSQL, Elasticsearch, DuckDB).
Experience with workflow orchestration tools like Airflow and at least one major public cloud platform (GCP preferred, AWS or Azure also acceptable).
Experience operating in fast-paced environments with direct impact on customer-facing products and business outcomes.
Comfortable owning challenging engineering problems and building reliable, maintainable software with a focus on quality and performance.
Familiar with cloud-native development practices, Linux environments, and leveraging AI-assisted development tools to improve productivity and code quality.