Data Architect

Our client is a financial institution providing the United States with a safe, flexible, and stable monetary system. They are seeking a Data Architect/Engineer. In this role you will modernize legacy data environments by implementing integrated cloud-based data infrastructure and analytical capabilities that improve data accessibility, security, workflow efficiency, and economic forecasting to support policymaking and Congressional mandates.

This role is onsite inWashington, DC. Per our client contract, candidates must be US Citizens. 

Responsibilities

• Design, develop, and implement modern data infrastructure and analytical capabilities to enhance economic forecasting and policymaking.
• Modernize legacy data environments through scalable and integrated technology solutions.
• Transform legacy and proprietary databases, along with fragmented data pipelines, into integrated cloud platforms and enterprise data integration systems.
• Implement collaboration tools that improve data accessibility, usability, and security.
• Streamline workflows to support operational efficiency and timely economic insights.
• Enable innovative analytical work through advanced data and technology capabilities.
• Support continued leadership in economic analysis and data-driven decision-making.
• Promote efficiency in meeting Congressional mandates and organizational objectives.
• Drive adoption of advanced tools, modern technologies, and best practices across the organization.

Qualifications

• Analyze data processes, applications, and source data to identify dependencies, anomalies, and implicit business rules impacting the division’s data management capabilities. Review and assess existing data models and processes to enhance and modernize current data architectures.
• Design, develop, and support robust data pipelines that ingest, transform, and deliver data from diverse sources to analytics platforms while ensuring high performance, scalability, and data integrity.
• Architect and implement ETL/ELT workflows using modern data engineering tools and frameworks to support large-scale data processing and economic analysis initiatives.
• Develop data solution architectures for economic policy and research initiatives, including conceptual models, integration strategies, and data sourcing approaches aligned with organizational research objectives and data strategy.
• Translate division and section requirements into scalable long-term information architecture solutions.
• Define technical specifications and implement database structures, including logical and physical data models, backup and recovery strategies, and security/access controls.
• Create and maintain comprehensive documentation for data structures, data flows, data dictionaries, and technical metadata.
• Partner with research and business stakeholders to enhance data models and processes that support analytics and visualization platforms, improving data accessibility and enabling data-driven decision-making across the organization.
• Establish and maintain processes and monitoring systems to ensure production data quality, accuracy, reliability, and availability for end users and dependent business operations.
• Identify, design, and implement process improvements, including automation of manual tasks, optimization of data delivery processes, and infrastructure redesigns to improve scalability, efficiency, and system performance.
• Contribute to the development of future-state data architecture standards, governance frameworks, best practices, and guiding principles.

Specific Requirements and Skills

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical discipline with at least 7 years of relevant experience; advanced degree preferred.
• Advanced proficiency in SQL and hands-on experience with relational database platforms such as PostgreSQL, Microsoft SQL Server, and MySQL.
• Advanced proficiency in Python, R, and other scripting languages commonly used for data engineering and analytics.
• Experience working with enterprise-scale data systems, including distributed computing, scalable data processing, data storage architecture, and optimization of high-volume workloads.
• Proven experience designing, developing, and automating ETL/ELT workflows and enterprise data integration pipelines.
• Experience building, optimizing, and maintaining scalable databases, data pipelines, and data processing frameworks.
• Hands-on 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-based environments.
• Experience processing, integrating, and analyzing both structured and unstructured data sources.
• Experience developing within Linux environments and utilizing source control platforms such as GitLab and/or GitHub.
• Strong experience performing root cause analysis on internal and external data and business processes to resolve business challenges and identify process improvement opportunities.
• Ability to design and communicate enterprise information architecture across conceptual, logical, and physical layers.
• Extensive experience designing and implementing database, data lake, and enterprise data platform solutions.
• Strong hands-on software engineering and implementation experience, including development, testing, deployment, and support of data applications and services.
• Excellent written and verbal communication skills with a strong customer-focused mindset.
• Exceptional analytical, troubleshooting, and problem-solving capabilities.

Additional Desired Skills and Experience

• Understanding of time-series data and associated analytical and forecasting methodologies.
• Experience working in research-driven environments and/or with economic or financial datasets.
• Experience with NoSQL and graph database technologies.
• Experience developing, training, deploying, and maintaining machine learning models and solutions.
• Working knowledge of cloud technologies and platforms such as AWS, Microsoft Azure, and Snowflake.
• Experience implementing enterprise data warehouses utilizing Change Data Capture (CDC) methodologies.
• Experience implementing and maintaining CI/CD pipelines and DataOps frameworks.
• Working knowledge of additional programming and scripting languages such as Java, Scala, JavaScript, or Perl.