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Mid-level Data Engineer role, metro locations, common tech stack attracts many qualified applicants.
Core data engineering skills transfer across industries, though enterprise domain and multi-cloud expectations increase specificity.
Explicit 4–7 years plus mandatory multi-cloud, PySpark, Airflow and domain experience narrows candidate pool.
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Design and build scalable, governed data products following data-as-a-product strategy and UDA standards.
Develop and optimize batch and near-real-time data pipelines using AWS (S3, Glue, MWAA, Athena/Redshift) and GCP (GCS, BigQuery, Dataproc, Dataflow).
Partner with business and analytics teams to translate requirements and apply data governance, quality, lineage, and security controls; mentor junior engineers.
4 to 7 years of experience in Data Engineering or Data Platforms.
Strong hands-on experience with AWS and GCP cloud platforms.
Proficiency in PySpark, SQL, Python and workflow orchestration tools like Airflow / Cloud Composer / MWAA.
Solid understanding of lakehouse architecture, data modeling, and experience with enterprise business processes (Finance, Supply Chain, Sales).
Experienced in building end-to-end scalable data architectures aligned with enterprise standards and multi-cloud environments.
Capable of translating complex business requirements into data engineering solutions and enforcing data governance frameworks.
Proven ability to mentor junior engineers and contribute to platform modernization and reusable engineering patterns.