Data Engineer-Spark,Scala

Data Engineer (Spark/Scala)

About the Role

We are seeking an experienced Data Engineer to design, build, and optimize complex data workflows across on-premises and cloud environments. This role requires deep hands-on expertise in Apache Spark, Databricks, and Scala/PySpark, along with strong SQL and Python skills, to build robust, high-performance data pipelines. You will work extensively on complex on-prem workflows, integrating data across multiple file systems and formats, migrating and modernizing legacy processes, and ensuring efficient, reliable data movement across heterogeneous environments.

Key Responsibilities

• Design, develop, and maintain large-scale data pipelines using Apache Spark, Databricks, Scala Spark, and PySpark
• Build and support complex on-premises data workflows, including migration/hybrid on-prem-to-cloud integration patterns
• Integrate data across diverse file systems (on-prem file shares, NAS, HDFS, S3) and formats JSON, Parquet, Fixed-Length, CSV, Excel, Avro
• Write efficient, optimized SQL for data extraction, transformation, and loading across relational databases
• Connect to and extract data efficiently from various source databases, tuning queries and pipelines for performance at scale
• Develop and maintain workflow orchestration using Airflow (or similar schedulers) for reliable, monitored pipeline execution
• Write clean, production-grade Python code for data processing, automation, and tooling
• Build and maintain unit/integration tests for data pipelines to ensure data quality and reliability
• Create and maintain clear technical documentation for pipelines, data flows, and system architecture
• Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data quality issues in complex, multi-system workflows
• Collaborate with cross-functional teams (data science, analytics, application engineering) to support downstream data consumption
• Support cloud integration efforts, particularly with Azure, as workloads evolve from on-prem to hybrid/cloud architectures

Required Qualifications

Primary Skills:

• Strong hands-on experience with Apache Spark and Databricks for large-scale data processing
• Proficiency with Amazon S3 for data storage and pipeline integration
• Strong SQL skills - query optimization, complex joins, performance tuning
• Proven experience integrating data across various file systems and formats: JSON, Parquet, Fixed-Length, CSV, Excel, Avro, etc.
• Strong knowledge of Scala Spark and PySpark for distributed data processing
• Strong Python programming skills for scripting, automation, and data engineering tasks
• Strong experience connecting to and efficiently extracting data from databases (relational/other), including performance-conscious extraction strategies
• Demonstrated experience working on complex on-prem data workflows (multi-system integration, legacy system data extraction, hybrid on-prem/cloud pipelines)
• Experience leveraging coding assistant tools and implementing AI agents to enhance development productivity and task execution.

Secondary Skills:

• Experience with Azure cloud services (storage, compute, data services)
• Experience with Apache Airflow for workflow orchestration and scheduling
• Experience writing automated tests for data pipelines (unit, integration, data quality checks)
• Strong documentation skills able to clearly document pipelines, data lineage, and technical designs

Good to Have:

• Working knowledge of Java

• Familiarity with React for building internal tooling/dashboards

• Experience with Prefect for workflow orchestration

• PBM (Pharmacy Benefit Management) / Healthcare domain knowledge

  Data Engineer (Spark/Scala)

  About the Role

  We are seeking an experienced Data Engineer to design, build, and optimize complex data workflows across on-premises and cloud environments. This role requires deep hands-on expertise in Apache Spark, Databricks, and Scala/PySpark, along with strong SQL and Python skills, to build robust, high-performance data pipelines. You will work extensively on complex on-prem workflows, integrating data across multiple file systems and formats, migrating and modernizing legacy processes, and ensuring efficient, reliable data movement across heterogeneous environments.

  Key Responsibilities

  • Design, develop, and maintain large-scale data pipelines using Apache Spark, Databricks, Scala Spark, and PySpark
  • Build and support complex on-premises data workflows, including migration/hybrid on-prem-to-cloud integration patterns
  • Integrate data across diverse file systems (on-prem file shares, NAS, HDFS, S3) and formats JSON, Parquet, Fixed-Length, CSV, Excel, Avro
  • Write efficient, optimized SQL for data extraction, transformation, and loading across relational databases
  • Connect to and extract data efficiently from various source databases, tuning queries and pipelines for performance at scale
  • Develop and maintain workflow orchestration using Airflow (or similar schedulers) for reliable, monitored pipeline execution
  • Write clean, production-grade Python code for data processing, automation, and tooling
  • Build and maintain unit/integration tests for data pipelines to ensure data quality and reliability
  • Create and maintain clear technical documentation for pipelines, data flows, and system architecture
  • Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data quality issues in complex, multi-system workflows
  • Collaborate with cross-functional teams (data science, analytics, application engineering) to support downstream data consumption
  • Support cloud integration efforts, particularly with Azure, as workloads evolve from on-prem to hybrid/cloud architectures

  Required Qualifications

  Primary Skills:

  • Strong hands-on experience with Apache Spark and Databricks for large-scale data processing
  • Proficiency with Amazon S3 for data storage and pipeline integration
  • Strong SQL skills - query optimization, complex joins, performance tuning
  • Proven experience integrating data across various file systems and formats: JSON, Parquet, Fixed-Length, CSV, Excel, Avro, etc.
  • Strong knowledge of Scala Spark and PySpark for distributed data processing
  • Strong Python programming skills for scripting, automation, and data engineering tasks
  • Strong experience connecting to and efficiently extracting data from databases (relational/other), including performance-conscious extraction strategies
  • Demonstrated experience working on complex on-prem data workflows (multi-system integration, legacy system data extraction, hybrid on-prem/cloud pipelines)
  • Experience leveraging coding assistant tools and implementing AI agents to enhance development productivity and task execution.

  Secondary Skills:

  • Experience with Azure cloud services (storage, compute, data services)
  • Experience with Apache Airflow for workflow orchestration and scheduling
  • Experience writing automated tests for data pipelines (unit, integration, data quality checks)
  • Strong documentation skills able to clearly document pipelines, data lineage, and technical designs

  Good to Have:

  • Working knowledge of Java

  • Familiarity with React for building internal tooling/dashboards

  • Experience with Prefect for workflow orchestration

  • PBM (Pharmacy Benefit Management) / Healthcare domain knowledge.

    Data Engineer (Spark/Scala)

    About the Role

    We are seeking an experienced Data Engineer to design, build, and optimize complex data workflows across on-premises and cloud environments. This role requires deep hands-on expertise in Apache Spark, Databricks, and Scala/PySpark, along with strong SQL and Python skills, to build robust, high-performance data pipelines. You will work extensively on complex on-prem workflows, integrating data across multiple file systems and formats, migrating and modernizing legacy processes, and ensuring efficient, reliable data movement across heterogeneous environments.

    Key Responsibilities

    • Design, develop, and maintain large-scale data pipelines using Apache Spark, Databricks, Scala Spark, and PySpark
    • Build and support complex on-premises data workflows, including migration/hybrid on-prem-to-cloud integration patterns
    • Integrate data across diverse file systems (on-prem file shares, NAS, HDFS, S3) and formats JSON, Parquet, Fixed-Length, CSV, Excel, Avro
    • Write efficient, optimized SQL for data extraction, transformation, and loading across relational databases
    • Connect to and extract data efficiently from various source databases, tuning queries and pipelines for performance at scale
    • Develop and maintain workflow orchestration using Airflow (or similar schedulers) for reliable, monitored pipeline execution
    • Write clean, production-grade Python code for data processing, automation, and tooling
    • Build and maintain unit/integration tests for data pipelines to ensure data quality and reliability
    • Create and maintain clear technical documentation for pipelines, data flows, and system architecture
    • Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data quality issues in complex, multi-system workflows
    • Collaborate with cross-functional teams (data science, analytics, application engineering) to support downstream data consumption
    • Support cloud integration efforts, particularly with Azure, as workloads evolve from on-prem to hybrid/cloud architectures

    Required Qualifications

    Primary Skills:

    • Strong hands-on experience with Apache Spark and Databricks for large-scale data processing
    • Proficiency with Amazon S3 for data storage and pipeline integration
    • Strong SQL skills - query optimization, complex joins, performance tuning
    • Proven experience integrating data across various file systems and formats: JSON, Parquet, Fixed-Length, CSV, Excel, Avro, etc.
    • Strong knowledge of Scala Spark and PySpark for distributed data processing
    • Strong Python programming skills for scripting, automation, and data engineering tasks
    • Strong experience connecting to and efficiently extracting data from databases (relational/other), including performance-conscious extraction strategies
    • Demonstrated experience working on complex on-prem data workflows (multi-system integration, legacy system data extraction, hybrid on-prem/cloud pipelines)
    • Experience leveraging coding assistant tools and implementing AI agents to enhance development productivity and task execution.

    Secondary Skills:

    • Experience with Azure cloud services (storage, compute, data services)
    • Experience with Apache Airflow for workflow orchestration and scheduling
    • Experience writing automated tests for data pipelines (unit, integration, data quality checks)
    • Strong documentation skills able to clearly document pipelines, data lineage, and technical designs

    Good to Have:

    • Working knowledge of Java

    • Familiarity with React for building internal tooling/dashboards

    • Experience with Prefect for workflow orchestration

    • PBM (Pharmacy Benefit Management) / Healthcare domain knowledge

      Data Engineer (Spark/Scala)

      About the Role

      We are seeking an experienced Data Engineer to design, build, and optimize complex data workflows across on-premises and cloud environments. This role requires deep hands-on expertise in Apache Spark, Databricks, and Scala/PySpark, along with strong SQL and Python skills, to build robust, high-performance data pipelines. You will work extensively on complex on-prem workflows, integrating data across multiple file systems and formats, migrating and modernizing legacy processes, and ensuring efficient, reliable data movement across heterogeneous environments.

      Key Responsibilities

      • Design, develop, and maintain large-scale data pipelines using Apache Spark, Databricks, Scala Spark, and PySpark
      • Build and support complex on-premises data workflows, including migration/hybrid on-prem-to-cloud integration patterns
      • Integrate data across diverse file systems (on-prem file shares, NAS, HDFS, S3) and formats JSON, Parquet, Fixed-Length, CSV, Excel, Avro
      • Write efficient, optimized SQL for data extraction, transformation, and loading across relational databases
      • Connect to and extract data efficiently from various source databases, tuning queries and pipelines for performance at scale
      • Develop and maintain workflow orchestration using Airflow (or similar schedulers) for reliable, monitored pipeline execution
      • Write clean, production-grade Python code for data processing, automation, and tooling
      • Build and maintain unit/integration tests for data pipelines to ensure data quality and reliability
      • Create and maintain clear technical documentation for pipelines, data flows, and system architecture
      • Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data quality issues in complex, multi-system workflows
      • Collaborate with cross-functional teams (data science, analytics, application engineering) to support downstream data consumption
      • Support cloud integration efforts, particularly with Azure, as workloads evolve from on-prem to hybrid/cloud architectures

      Required Qualifications

      Primary Skills:

      • Strong hands-on experience with Apache Spark and Databricks for large-scale data processing
      • Proficiency with Amazon S3 for data storage and pipeline integration
      • Strong SQL skills - query optimization, complex joins, performance tuning
      • Proven experience integrating data across various file systems and formats: JSON, Parquet, Fixed-Length, CSV, Excel, Avro, etc.
      • Strong knowledge of Scala Spark and PySpark for distributed data processing
      • Strong Python programming skills for scripting, automation, and data engineering tasks
      • Strong experience connecting to and efficiently extracting data from databases (relational/other), including performance-conscious extraction strategies
      • Demonstrated experience working on complex on-prem data workflows (multi-system integration, legacy system data extraction, hybrid on-prem/cloud pipelines)
      • Experience leveraging coding assistant tools and implementing AI agents to enhance development productivity and task execution.

      Secondary Skills:

      • Experience with Azure cloud services (storage, compute, data services)
      • Experience with Apache Airflow for workflow orchestration and scheduling
      • Experience writing automated tests for data pipelines (unit, integration, data quality checks)
      • Strong documentation skills able to clearly document pipelines, data lineage, and technical designs

      Good to Have:

      • Working knowledge of Java
      • Familiarity with React for building internal tooling/dashboards
      • Experience with Prefect for workflow orchestration
      • PBM (Pharmacy Benefit Management) / Healthcare domain knowledge

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