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

• Design, develop, and maintain scalable data pipelines using Apache Spark, Databricks, Scala Spark, and PySpark.
• Build and support complex on-premises data workflows and hybrid on-prem-to-cloud data integration solutions.
• Integrate data across HDFS, NAS, on-prem file shares, Amazon S3, and other storage platforms.
• Work with multiple data formats including JSON, Parquet, CSV, Avro, Fixed-Length, and Excel.
• Develop optimized SQL queries for data extraction, transformation, and loading.
• Connect to multiple relational and non-relational databases and implement performance-efficient data extraction strategies.
• Develop and maintain workflow orchestration using Apache Airflow or similar scheduling tools.
• Write clean, production-grade Python code for data processing, automation, and engineering utilities.
• Develop unit, integration, and data-quality tests for data pipelines.
• Troubleshoot pipeline failures, performance bottlenecks, data quality issues, and complex multi-system integration problems.
• Support migration and modernization of legacy on-premises data processes to hybrid/cloud environments.
• Collaborate with Data Scientists, Analysts, Application Engineers, and other stakeholders.
• Create technical documentation covering pipelines, data flows, architecture, and data lineage.
• Support cloud integration initiatives, particularly across Azure environments.
• Leverage coding assistants and AI agents to improve development productivity and automate engineering tasks.

Required Skills

• Strong hands-on experience with Apache Spark and Databricks.
• Strong experience with Scala/Spark Scala and PySpark.
• Strong Python programming skills.
• Strong SQL, including complex joins, query optimization, and performance tuning.
• Hands-on experience with Amazon S3.
• Experience working with HDFS, NAS, on-prem file systems, and cloud storage.
• Strong experience handling JSON, Parquet, CSV, Avro, Fixed-Length, and Excel data formats.
• Experience extracting data efficiently from multiple databases.
• Strong understanding of complex on-premises data workflows and multi-system integrations.
• Experience building hybrid on-prem/cloud data pipelines.
• Strong troubleshooting and production support skills. Secondary Skills
• Azure cloud services.
• Apache Airflow or similar workflow orchestration tools.
• Automated unit and integration testing.
• Data quality validation and monitoring.
• Data lineage and technical documentation. Good to Have
• Working knowledge of Java.
• Experience with Prefect.
• Familiarity with React for internal tools or dashboards.
• Experience using AI coding assistants and AI agents.
• 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. Skills: cloud,apache spark,scala