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 Client 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 redesigning the R&S division's data architecture to support our next generation of data initiatives.
Requirements:
The candidate shall also demonstrate the below knowledge and experience:
Analyze data processes, applications, and source data to understand dependencies, anomalies, and implicit business rules that impact the division's ability to manage data.
Review and analyze existing data models and processes to optimize and modernize current data architectures.
Design, develop, and maintain robust data pipelines that ingest, transform, and deliver data from multiple sources to analytics platforms, ensuring optimal performance and data integrity throughout the process.
Architect and implement ETL/ELT workflows using modern data engineering tools and frameworks to support large-scale data processing for economic analysis.
Create data solution designs for economic policy and research projects, including conceptual models, integration models, and sourcing strategies, in alignment with the division's research needs and data strategy.
Translate division and section requirements into long-term information architecture solutions.
Define specifications and implement database structures, including logical and physical data models, backup and recovery procedures, and access security controls.
Develop and maintain formal documentation of data structures, data flows, data dictionaries, and technical metadata.
Collaborate with research and business teams to improve data models and data processes that support analytics and visualization tools, increasing data accessibility and fostering data driven decision making across the organization.
Implement processes and systems to monitor data quality, ensuring production data is accurate, reliable, and available for end users and dependent business processes.
Identify, design, and implement internal process improvements, including automation of manual processes, optimization of data delivery, and redesign of infrastructure to improve scalability and performance.
Participate in the development of future-state data architecture standards, guidelines, and principles.
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.
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 with NoSQL and graph database technologies.
Experience developing, training, deploying, and maintaining machine learning models.
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.
