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Metro-based, mid-level data-platform role with common Spark/Java skills and 4+ years experience.
Core data engineering skills are transferable, but fintech/healthcare platform context adds moderate domain specificity.
Explicit 4+ years and mandatory Spark, Java, cloud, and data platform experience increases filtering strictness.
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Design, develop, and optimize scalable data pipelines using Apache Spark, focusing on batch and streaming data processing.
Build and maintain data platform layers and data-driven APIs/microservices in Java to provide reliable, low-latency real-time data access.
Take end-to-end ownership from design through production, ensuring performance, data quality, observability, and integration with cloud platforms (Azure preferred).
4+ years software engineering experience with focus on data platforms and/or distributed systems.
Strong hands-on experience with Apache Spark or Scala or PySpark and programming skills in Java (preferred) / Scala / Python.
Experience in building ETL/ELT pipelines, backend REST/microservices APIs, and system design with distributed systems knowledge.
Experience with cloud platforms (Azure/AWS/GCP) and familiarity with workflow orchestration tools (Airflow, Dagster) required.
Experienced engineer operating at the intersection of data engineering and backend platform engineering skilled in scalable distributed systems and API design.
Proficient with Spark optimization, data architecture decisions (Lakehouse, data mesh), and cloud-native development (preferably Azure).
Able to engage with product and business stakeholders to deliver data-driven solutions with strong ownership and mentorship capabilities.