Data Engineer, ETL & Big Data
Overview
You will own end-to-end data pipeline development and modern data infrastructure across client sites in DC, MD, PA, DE, and VA. You will design and maintain scalable ETL processes using Python, SQL, and Spark to support analytics and machine learning. You will work on a cross-functional team of data engineers and analysts, delivering data solutions at enterprise scale. This contract role offers hands-on work with modern cloud platforms like AWS and containerization with Docker.
What You'll Do8
- 1Design and implement scalable ETL pipelines using Python and Spark to process large datasets from multiple sources.
- 2Build and maintain data models and schemas in SQL-based databases for optimal performance and reliability.
- 3Develop automated data validation and monitoring tools to ensure data quality and integrity.
- 4Collaborate with data scientists and analysts to shape data architecture for machine learning workflows.
- 5Optimize pipeline performance and reduce latency using AWS services such as S3, Redshift, and Kinesis.
- 6Containerize data applications with Docker and manage orchestration using Airflow.
- 7Participate in on-call rotation to troubleshoot pipeline failures and restore service quickly.
- 8Document data flows and system designs to support maintenance and knowledge transfer.
Requirements6
- 15+ years of experience in data engineering or related field, with proven track record of building production pipelines.
- 2Proficiency in Python and SQL with hands-on experience in Spark for data processing.
- 3Experience with cloud data platforms, specifically AWS (e.g., S3, Redshift, Glue).
- 4Familiarity with containerization tools like Docker and workflow orchestration using Airflow.
- 5Strong understanding of data modeling, ETL best practices, and data warehousing concepts.
- 6Excellent problem-solving skills and ability to work independently in a fast-paced environment.
Salary Insight
Salary not disclosed in listing
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