Data Engineering Architect - Hyderabad / Secunderabad, Telangana - Virtusa

    Virtusa
    Virtusa Hyderabad / Secunderabad, Telangana

    1 day ago

    Full time ₹1,800,000 - ₹4,200,000 (INR) per year *
    Description
    • Proficiency in PySpark for distributed data processing and transformation.
    • Solid experience with AWS Glue for ETL jobs and managing data workflows.
    • Hands-on experience with AWS Data Pipeline (DPL) for workflow orchestration.
    • Strong experience with AWS services such as S3, Lambda, Redshift, RDS, and EC2.

    Technical Skills:

    • Proficiency in Python and PySpark for data processing and transformation tasks.
    • Deep understanding of ETL concepts and best practices.
    • Familiarity with AWS Glue (ETL jobs, Data Catalog, and Crawlers).
    • Experience building and maintaining data pipelines with AWS Data Pipeline or similar orchestration tools.
    • Familiarity with AWS S3 for data storage and management, including file formats (CSV, Parquet, Avro).
    • Strong knowledge of SQL for querying and manipulating relational and semi-structured data.
    • Experience with Data Warehousing and Big Data technologies, specifically within AWS.
    • Additional Skills:
    • Experience with AWS Lambda for serverless data processing and orchestration.
    • Understanding of AWS Redshift for data warehousing and analytics.
    • Familiarity with Data Lakes, Amazon EMR, and Kinesis for streaming data processing.
    • Knowledge of data governance practices, including data lineage and auditing.
    • Familiarity with CI/CD pipelines and Git for version control.

    Experience with Docker and containerization for building and deploying applications.

    Design and Build Data Pipelines: Design, implement, and optimize data pipelines on AWS using PySpark, AWS Glue, and AWS Data Pipeline to automate data integration, transformation, and storage processes.

    ETL Development: Develop and maintain Extract, Transform, and Load (ETL) processes using AWS Glue and PySpark to efficiently process large datasets.

    Data Workflow Automation: Build and manage automated data workflows using AWS Data Pipeline, ensuring seamless scheduling, monitoring, and management of data jobs.

    Data Integration: Work with different AWS data storage services (e.g., S3, Redshift, RDS) to ensure smooth integration and movement of data across platforms.

    Optimization and Scaling: Optimize and scale data pipelines for high performance and cost efficiency, utilizing AWS services like Lambda, S3, and EC2.

    * This salary range is an estimation made by beBee
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Data engineering architect