Data Engineer (Spark/Scala)

We are looking for an experienced Data Engineer (Spark/Scala) with strong hands-on expertise in Apache Spark, Databricks, Scala, PySpark, Python, and SQL.

The role involves designing and developing large-scale data pipelines across on-premises and cloud environments, working with multiple file systems and data formats, modernizing legacy workflows, and supporting hybrid data architectures.

Key Responsibilities

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Design, develop, and maintain scalable data pipelines using Apache Spark, Databricks, Scala Spark, and PySpark.

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Build and support complex on-premises data workflows and hybrid on-prem-to-cloud data integration solutions.

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Integrate data across HDFS, NAS, on-prem file shares, Amazon S3, and other storage platforms.

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Work with multiple data formats including JSON, Parquet, CSV, Avro, Fixed-Length, and Excel.

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Develop optimized SQL queries for data extraction, transformation, and loading.

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Connect to multiple relational and non-relational databases and implement performance-efficient data extraction strategies.

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Develop and maintain workflow orchestration using Apache Airflow or similar scheduling tools.

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Write clean, production-grade Python code for data processing, automation, and engineering utilities.

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Develop unit, integration, and data-quality tests for data pipelines.

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Troubleshoot pipeline failures, performance bottlenecks, data quality issues, and complex multi-system integration problems.

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Support migration and modernization of legacy on-premises data processes to hybrid/cloud environments.

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Collaborate with Data Scientists, Analysts, Application Engineers, and other stakeholders.

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Create technical documentation covering pipelines, data flows, architecture, and data lineage.

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Support cloud integration initiatives, particularly across Azure environments.

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Leverage coding assistants and AI agents to improve development productivity and automate engineering tasks.

Required Skills

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Strong hands-on experience with Apache Spark and Databricks.

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Strong experience with Scala/Spark Scala and PySpark.

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Strong Python programming skills.

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Strong SQL, including complex joins, query optimization, and performance tuning.

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Hands-on experience with Amazon S3.

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Experience working with HDFS, NAS, on-prem file systems, and cloud storage.

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Strong experience handling JSON, Parquet, CSV, Avro, Fixed-Length, and Excel data formats.

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Experience extracting data efficiently from multiple databases.

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Strong understanding of complex on-premises data workflows and multi-system integrations.

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Experience building hybrid on-prem/cloud data pipelines.

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Strong troubleshooting and production support skills.

Secondary Skills

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Azure cloud services.

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Apache Airflow or similar workflow orchestration tools.

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Automated unit and integration testing.

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Data quality validation and monitoring.

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Data lineage and technical documentation.

Good to Have

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Working knowledge of Java.

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Experience with Prefect.

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Familiarity with React for internal tools or dashboards.

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Experience using AI coding assistants and AI agents.

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PBM / Pharmacy Benefit Management / Healthcare domain experience.

Preferred Candidate Profile

Candidates with strong experience in Scala + Spark + Databricks + PySpark, combined with on-premises data engineering and hybrid cloud integration, will be preferred.

Key Skills: Apache Spark, Scala, Spark Scala, Databricks, PySpark, Python, SQL, Amazon S3, HDFS, Airflow, Azure, On-Premise Data Engineering, ETL, Data Pipelines.