AWS Data Engineer
3 weeks ago
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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