Data Engineer

Candidates will be disqualified if the following criteria are not met: PLEASE READ AND UNDERSTAND. • Employment Type: No C2C (Corp-to-Corp), W2 contract only. No referral fees will be entertained. • Work Authorization: US CITIZEN and Greencard ONLY!!! • Remote with Ocassional Site visit • 2+ years’ experience with tools such as Databricks, Collibra, and Starburst. • 3+ years’ experience with Python and PySpark. • Experience using Jupyter notebooks, including coding and unit testing. • Recent accomplishments working with relational and NoSQL data stores, methods, and approaches (STAR, Dimensional Modeling). • 2+ years of experience with a modern data stack (Object stores like S3, Spark, Airflow, Lakehouse architectures, real-time databases) and cloud data warehouses such as RedShift, Snowflake. Data is at the center of the operations and right now we are working to modernize how we manage and leverage it. The Common Data Platform (CDP) is an exciting new, multi-district program to create a cloud based, end-to-end data management platform to reduce data cost and improve user experience. Initially CDP was developed in partnership with the Supervision and Regulation (bank examination) business, but this system is now positioned to become “the standard” System data management platform. The CDP program team is comprised of members from across the entire System using the Scaled Agile Framework for Enterprises (SAFe) to provide incremental and iterative business value. This is a unique and exciting opportunity to be part of a team on the leading edge of data management and consumption. You’ll join a world-class team in a dynamic environment that will create a lasting impact on the entire System. As a Data Engineer, this contingent worker will be responsible for collecting, parsing, managing, analyzing, and visualizing large sets of data to turn information into actionable insights. They will work across multiple platforms to ensure that data pipelines are scalable, repeatable, and secure, capable of serving multiple users. Essential Responsibilities: • Design, develop, and maintain robust and efficient data pipelines to ingest, transform, catalog, and deliver curated, trusted, and quality data from disparate sources into our Common Data Platform. • Actively participate in Agile rituals and follow Scaled Agile processes as set forth by the CDP Program team. • Deliver high-quality data products and services following Safe Agile Practices. • Proactively identify and resolve issues with data pipelines and analytical data stores. • Deploy monitoring and alerting for data pipelines and data stores, implementing auto-remediation where possible to ensure system availability and reliability. • Employ a security-first, testing, and automation strategy, adhering to data engineering best practices. • Collaborate with cross-functional teams, including product management, data scientists, analysts, and business stakeholders, to understand their data requirements and provide them with the necessary infrastructure and tools. • Keep up with the latest trends and technologies, evaluating and recommending new tools, frameworks, and technologies to improve data engineering processes and efficiencies. Requirements: • Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent experience. • 2+ years’ experience with tools such as Databricks, Collibra, and Starburst. • 3+ years’ experience with Python and PySpark. • Experience using Jupyter notebooks, including coding and unit testing. • Recent accomplishments working with relational and NoSQL data stores, methods, and approaches (STAR, Dimensional Modeling). • 2+ years of experience with a modern data stack (Object stores like S3, Spark, Airflow, Lakehouse architectures, real-time databases) and cloud data warehouses such as RedShift, Snowflake. • Overall data engineering experience across traditional ETL & Big Data, either on-prem or Cloud. • Data engineering experience in AWS (any CFS2/EDS) highlighting the services/tools used. • Experience building end-to-end data pipelines to ingest and process unstructured and semi-structured data using Spark architecture.