Neuronet Solutions

PROJECT DESCRIPTION:

Design, develop and implement modern data infrastructure and analytical capabilities to enhance economic forecasting and policymaking, with specific focus on modernizing legacy data environments. The program transforms legacy and proprietary databases, and fragmented data pipelines into integrated cloud platforms, enterprise data integration systems, and collaboration tools that improve data accessibility and security. These initiatives streamline workflows, enable timely economic insights, and support innovative analytical work. This modernization effort ensures continued leadership in economic analysis, promotes efficiency in meeting Congressional mandates, and supports adoption of advanced tools and best practices organization-wide.

BACKGROUND:    

The Data Architecture, Technology, and Analytics (DATA) section is tasked with transforming

how the Federal Reserve Board’s Division of Research & Statistics (R&S) ingests, organize,

uses, and visualizes data.

The Data Architecture, Technology, and Analytics (DATA) section is looking for an experienced

detailed oriented Data Architect/Engineer who will be responsible for expanding and optimizing

our data and data pipeline architecture, as well as optimizing data flow and collection for

economic policy and research teams.  The ideal candidate is an experienced hands-on data

modeler with working knowledge of database design and administration, data pipeline building,

and data wrangling who enjoys improving existing data systems and/or building them from the

ground up. The Data Architect/Engineer will support our economists and technical experts and

will ensure optimal data delivery architecture is designed and developed.  They must have a

service mindset, be self-directed, and be comfortable supporting the data needs of multiple teams

and systems.  The right candidate will be excited by the prospect of optimizing or even re

designing the R&S division’s data architecture to support our next generation of data initiatives.

Specific Requirements and Skills

• Bachelor’s degree in computer science, Information Technology, Engineering or a related technical field and at least 7 years of related experience; advanced degree preferred.

• Advanced working knowledge of SQL and experience working with relational database platforms including PostgreSQL, Microsoft SQL Server, and MySQL.

• Advanced working knowledge of Python, R, and other scripting languages used for data engineering and analytics.

• Experience working with large-scale data systems, including distributed computing, scalable data processing, data storage architecture, and optimization of high-volume data workloads.

• Experience designing, developing, and automating ETL/ELT workflows and data integration pipelines.

• Experience building, optimizing, and maintaining scalable databases, data pipelines and data processing frameworks.

• Experience with workflow orchestration and pipeline automation tools such as Apache Airflow, Prefect, Dagster, or AWS Step Functions.

• Experience migrating workflows and data pipelines between on-premises and cloud environments.

• Experience processing, analyzing, and integrating structured and unstructured data sources.

• Experience developing in Linux environments and using source control platforms such as GitLab and/or GitHub.

• Experience performing root cause analysis on internal and external data and business  processes to answer business questions and identify opportunities for improvement.

• Ability to design and communicate enterprise information architecture at conceptual, logical, and physical levels.

• In-depth experience designing and implementing database, data lake, and enterprise data platform solutions.

• Strong hands-on software engineering and implementation experience, including development, testing, and deployment of data applications and services.

• Excellent oral and written communication skills with a strong customer service orientation.

• Exceptional analytical, problem-solving, and troubleshooting skills.

• Experience with NoSQL and graph database technologies.

• Working experience with cloud technologies such as AWS, Microsoft Azure, and Snowflake.

• Experience implementing data warehouses utilizing Change Data Capture (CDC) methodologies.

• Experience implementing and maintaining CI/CD pipelines and DataOps platforms.

• Working knowledge of additional programming and scripting languages such as Java, Scala, JavaScript, or Perl. 

Additional Desirable Skills/Experience Include

• Understanding of time series data and related analytical and forecasting techniques.

• Experience working in a research environment and/or with economic or financial data.  

• Experience developing, training, deploying, and maintaining machine learning models