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Mid-level data engineer role, metro location, broad skills and popular title increases candidate competition.
Core data engineering skills are transferable, though media metadata and lakehouse experience adds moderate domain specificity.
Explicit 4–6 years plus many mandatory data and backend technologies increases filter strictness.
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Design, develop, and maintain scalable, high-performance data pipelines and backend systems managing large-scale datasets with fast refresh cycles.
Ensure data governance with frameworks for data lineage, quality, traceability, and system consistency.
Collaborate cross-functionally to translate business requirements into technical solutions and mentor junior engineers.
4 to 6 years of professional experience in Backend and Data Engineering involving large-scale datasets and real-time event processing.
Advanced programming skills in Python, Java, or Scala and experience with distributed data systems like Spark or Flink.
Experience building and maintaining scalable RESTful APIs and knowledge of backend frameworks.
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Strong operational expertise in designing cost-efficient, low-latency, resilient distributed data architectures.
Experience working with cloud platforms (AWS, Azure, GCP), distributed storage systems (HDFS, S3), and Lakehouse architectures (Delta Lake, Paimon).
Ability to drive quality through design/code reviews and implement Agile/CI-CD development practices.