Data Engineer

Expert In Recruitment Solutions

  • Malvern, PA
  • 30+ days ago

    Highlights

    The Data Engineer will be responsible for building and maintaining the data pipelines that process data from CSV files through various transformation stages, following the Architect's data model designs. This role requires hands-on experience with data transformations, working with large datasets, and implementing robust ETL processes within AWS.

    Numbers & Facts

    LocationMalvern, PA

    Description

    The Data Engineer will be responsible for building and maintaining the data pipelines that process data from CSV files through various transformation stages, following the Architect's data model designs. This role requires hands-on experience with data transformations, working with large datasets, and implementing robust ETL processes within AWS.
    Job Duties
    Key Responsibilities
    • Develop ETL pipelines to process and transform data across bronze, silver, and gold layers.
    • Implement data cleansing, validation, and transformation processes to support analytics-ready datasets.
    • Collaborate with the Sr. Data Engineer and Data Architect to ensure alignment with data models.
    • Optimize data workflows to improve processing times and resource utilization.
    Job Requirements
    Skills and Qualifications:
    • Bachelor's degree in Data Engineering, Computer Science, or related field.
    • 3-5 years of experience in data engineering, preferably within AWS environments.
    • Proficiency in PySpark, SQL, AWS Glue, and S3.
    • Robust problem-solving skills and attention to detail.
    • Desired Skills & Experience
    • Data Engineer
    • Data Analysis, Data Warehouse

    Job Responsibilities
    Key Responsibilities
    • Develop ETL pipelines to process and transform data across bronze, silver, and gold layers.
    • Implement data cleansing, validation, and transformation processes to support analytics-ready datasets.
    • Collaborate with the Sr. Data Engineer and Data Architect to ensure alignment with data models.
    • Optimize data workflows to improve processing times and resource utilization.

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