AWS Data Engineer

3 weeks ago


Newark, New Jersey, United States Iris Software Inc. Full time

We are seeking an experienced AWS Data Engineer to join our dynamic team at Iris Software Inc. The ideal candidate will have hands-on experience in building scalable, high-performance data pipelines using AWS services.

Key Responsibilities:

  • ETL Development: Design, develop, and maintain ETL pipelines for transforming large datasets from various data sources into structured formats for analysis and reporting.
  • Data Warehouse Management: Implement and optimize data warehousing solutions using AWS Redshift and other AWS data services to ensure efficient storage, retrieval, and analysis of large datasets.
  • Data Lake Architecture: Build and manage data lakes using AWS S3, AWS Glue, and Lake Formation to ingest, store, and process structured and unstructured data.
  • Serverless Data Processing: Leverage AWS Lambda for building serverless data workflows and real-time data processing pipelines.
  • Data Processing: Utilize Python, PySpark, and SQL for data processing, transformation, and automation of data pipelines.
  • Collaboration: Work closely with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data requirements and deliver high-quality solutions.
  • Monitoring & Optimization: Monitor data pipelines and data workflows for performance, quality, and efficiency; optimize resource usage to minimize costs.
  • Data Governance & Security: Implement best practices for data governance, data quality, and data security within the AWS ecosystem.

Key Qualifications:

  • Experience in data engineering, with a strong focus on AWS services and building data pipelines, data lakes, and data warehouses.
  • AWS Expertise: Proficient in AWS services including S3, Redshift, Glue, Athena, Lambda, EMR, and Lake Formation.
  • ETL & Data Warehousing: Hands-on experience with designing and managing ETL processes, building data warehouses, and optimizing SQL queries for high performance in distributed environments.
  • Programming Skills: Strong programming skills in Python and SQL, with experience in PySpark for large-scale data processing.
  • Data Lakes: Experience building and managing scalable, secure data lakes on AWS.
  • Serverless Architecture: Familiarity with AWS Lambda for creating serverless data processing workflows.
  • Data Management: Experience with data governance, data quality frameworks, and securing data at rest and in transit.
  • Problem Solving: Strong analytical and problem-solving skills, with the ability to troubleshoot and optimize data pipelines and workflows.
  • Communication: Excellent collaboration and communication skills to work effectively with technical and non-technical teams.

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