Tech Stack

Description

You will work with a small team to build complex data flows for a custom application, focusing on data pipelines and ETL workflows at scale. You'll design solutions with minimal oversight, debug data quality issues, and contribute to documentation and best practices. This role helps drive positive change for US federal government missions.

Requirements

• Minimum of 5 years' experience with Apache Spark, PySpark, using orchestration tools to deploy data pipelines, advanced Python skills (including Pandas, NumPy)
• Building end-to-end data pipelines leveraging Python, Docker, Podman, AWS S3, Lambda, Step functions, Apache Iceberg, Airflow, etc.
• SQL (with Trino), NoSQL, DynamoDB, containerizing and deploying applications in cloud environments like AWS
• Working with MySQL and PostgreSQL including performance tuning, schema design, and query optimization for complex, analytical workloads
• Leveraging industry standard tools for code control (Git, IaaC control, etc.)
• Working with data catalogs, tracking data lineage, and handling a variety of data formats, including Geospatial
• Using Bash scripting for automation and data processing tasks
• Integrating Al/ML services and models

Responsibilities

• Work with stakeholders to understand data requirements, assess feasibility, and design appropriate solutions with minimal oversight
• Leverage strong problem-solving and debugging skills for data quality issues, pipeline failures, and performance bottlenecks
• Leverage a background in large-scale data migration or platform modernization efforts
• Contribute to data engineering documentation, best practices, and design patterns