Position Overview
The Data Architecture, Technology, and Analytics (DATA) team within the Federal Reserve Board’s Division of Research & Statistics (R&S) is responsible for transforming how enterprise data is ingested, organized, analyzed, and visualized to support economic research and policy decision-making.
We are seeking a highly skilled Data Architect / Data Engineer to design, develop, and optimize modern data architectures, enterprise data platforms, and scalable data pipelines. This individual will play a critical role in supporting economists, researchers, analysts, and technical teams by ensuring efficient, reliable, and scalable access to data across the organization.
The ideal candidate is a hands-on data professional with deep expertise in data modeling, database architecture, ETL/ELT development, cloud platforms, and enterprise data management. This role requires strong analytical capabilities, excellent communication skills, and a passion for building data solutions that enable advanced research and business intelligence.

Key Responsibilities
Design, develop, and maintain enterprise-scale data architectures and data platforms.
Build and optimize scalable ETL/ELT pipelines for ingestion, transformation, and delivery of structured and unstructured data.
Develop conceptual, logical, and physical data models aligned with enterprise architecture standards.
Architect and manage relational databases, data warehouses, data lakes, and modern data ecosystems.
Support migration of data workflows and pipelines between on-premises and cloud environments.
Implement workflow orchestration and automation using tools such as Airflow, Prefect, Dagster, or AWS Step Functions.
Perform data integration across multiple internal and external data sources.
Design and implement Change Data Capture (CDC) solutions for enterprise data warehousing initiatives.
Optimize database performance, scalability, reliability, and data processing workloads.
Collaborate with economists, researchers, analysts, and technical stakeholders to understand business requirements and deliver effective data solutions.
Conduct root cause analysis of data issues and identify opportunities for process improvement and automation.
Implement and maintain CI/CD pipelines and DataOps practices for data engineering projects.
Support machine learning and advanced analytics initiatives through efficient data infrastructure and model deployment frameworks.
Ensure data governance, security, quality, and compliance standards are followed across all solutions.
Document architecture designs, technical specifications, and operational procedures.

Required Qualifications
Education
Bachelor’s Degree in Computer Science, Information Technology, Engineering, Data Science, or a related technical field.
Master’s Degree or other advanced degree is preferred.
Experience
Minimum 7+ years of experience in Data Engineering, Data Architecture, Database Engineering, or related fields.
Proven experience designing and implementing enterprise data platforms and large-scale data solutions.

Required Technical Skills
Data Engineering & Architecture
Strong expertise in data architecture, data modeling, and enterprise information architecture.
Experience designing conceptual, logical, and physical data models.
Extensive experience building and maintaining scalable data pipelines and processing frameworks.
Experience implementing enterprise data warehouses, data lakes, and modern analytics platforms.
Strong understanding of Change Data Capture (CDC) methodologies.
Databases
Advanced SQL expertise.
Hands-on experience with:
PostgreSQL
Microsoft SQL Server
MySQL
Experience with database administration, optimization, and performance tuning.
Programming & Scripting
Advanced proficiency in:
Python
R
Strong scripting and automation experience.
Experience with additional programming languages such as:
Java
Scala
JavaScript
Perl
ETL/ELT & Workflow Automation
Experience designing and automating ETL/ELT processes.
Hands-on experience with workflow orchestration tools such as:
Apache Airflow
Prefect
Dagster
AWS Step Functions
Cloud & Modern Data Platforms
Experience working with:
AWS
Microsoft Azure
Snowflake
Experience migrating applications and data pipelines between on-premises and cloud environments.
DevOps & DataOps
Experience implementing and maintaining:
CI/CD pipelines
DataOps frameworks
Experience with Git-based source control platforms:
GitHub
GitLab
Big Data & Analytics
Experience with:
Distributed computing frameworks
Large-scale data processing systems
High-volume data workloads
Experience processing structured and unstructured datasets.
Linux & Infrastructure
Strong development and deployment experience in Linux environments.
Preferred Qualifications
Experience working with economic, financial, or regulatory datasets.
Experience supporting research organizations or data-driven policy environments.
Understanding of time-series data modeling, forecasting, and statistical analysis techniques.
Experience with NoSQL technologies and Graph Databases.
Experience developing, deploying, and maintaining Machine Learning models.
Knowledge of enterprise data governance and metadata management frameworks.
Experience supporting advanced analytics and AI-driven data initiatives.
Soft Skills
Excellent verbal and written communication skills.
Strong stakeholder management and customer service mindset.
Exceptional analytical and problem-solving abilities.
Ability to work independently and manage multiple priorities simultaneously.
Strong troubleshooting and root-cause analysis skills.
Detail-oriented with a focus on quality and continuous improvement.