Data Architect

Federal Reserve System•St. Louis, MO

5d•$140,000 - $170,000•Hybrid

About The Position

The Federal Reserve Bank of St. Louis is seeking a Research Platform Architect to lead the technical vision and long-term architecture of its global economic research platforms such as FRED and FRASER. These platforms provide access to hundreds of thousands of economic time-series datasets, historical archives, and research publications used by millions of economists, policymakers, researchers, students, and developers worldwide. The Data Architect will lead the design and modernization of the data architecture that powers the economic data platforms of St. Louis based Research applications such as FRED. These platforms manage massive collections of economic time-series data, historical revisions, digitized archival documents, and research publications. This role focuses on designing scalable data platforms, data models, and data governance frameworks to support long-term growth, advanced analytics, and global access to economic data. It will leverage in-depth knowledge of modern data technologies, industry frameworks, data security best practices and emerging innovations in AI/ML and data science to accelerate value, delivery and outcomes for the business. The Data Architect will partner closely with the Application/Enterprise Architect to ensure seamless integration between data platforms and application services.

Requirements

7+ years of experience in data architecture, data engineering or database design and large-scale enterprise cloud data implementations in complex, highly regulated environments.
Deep understanding of modern data technology stacks, cloud data platforms (AWS preferred), and enterprise software solutions.
Demonstrated experience designing, architecting and supporting external / public-facing applications with a strong emphasis on scalability, security, availability and performance for external customer-facing platforms.
Demonstrated knowledge of and leading adoption of industry best practices in the areas of DataOps and modern data stack tools, data governance, SQL and other query tools and knowledge of data privacy regulations.
Demonstrated experience in cloud data architecture skills including designing, architecting and delivering modern cloud data platforms to ingest, process, store and expose data across the enterprise.
Expert knowledge of data modeling techniques and tools, data lifecycle management and integration patterns to ensure that data flows are consistent, reliable and reusable across services and solutions.
Experience with data storage technologies, partitioning strategies, caching, data access patterns, and robust security strategies enforcing privacy and regulatory requirements including designing standardized approaches for data classification, access control, encryption, retention and auditability.
Knowledge of data warehousing concepts and tools, big data technologies, machine learning and AI data requirements.
Outstanding communication and collaboration skills, including translating technical topics and risks into business teams.
Ability to personalize communication to audience from technologists to executive leadership.
Extensive, in-depth experience implementing highly complex technology projects in a cross-functional matrix environment with demonstrated ability to drive consensus and deliver results.
Examples of leading through influence and successfully resolving conflicting priorities are required.

Nice To Haves

Experience working with time-series data platforms or analytical datasets.
Experience with distributed query engines and large-scale data processing systems.
Experience with data catalog and metadata management platforms.
Familiarity with economic datasets or research data environments.
Experience supporting public data access or research-oriented platforms.
Industry-related certifications in one or more of the above areas are desired.

Responsibilities

Define and evolve the target data architecture supporting economic data systems.
Design scalable storage architectures capable of supporting billions of time-series observations and historical revisions.
Develop metadata schemas supporting dataset discoverability, lineage, and governance.
Standardize dataset structures and metadata across multiple platforms.
Implement scalable data processing frameworks capable of supporting growing dataset volumes.
Improve indexing and metadata strategies that support dataset search and discovery.
Enable advanced analytical capabilities for researchers and developers.
Partner with the Application Architect to optimize data access patterns for APIs and applications.