Senior Data Architect

Ampcus Inc.•Washington, DC

2h•Onsite

About The Position

Ampcus Inc. is seeking a Senior Data Architect to design, develop, and implement modern data infrastructure and analytical capabilities. This role focuses on enhancing economic forecasting and policymaking by modernizing legacy data environments. The program involves transforming legacy databases and data pipelines into integrated cloud platforms and enterprise data integration systems to improve data accessibility and security. The goal is to streamline workflows, enable timely economic insights, support innovative analytical work, and ensure continued leadership in economic analysis.

Requirements

Bachelor’s degree in computer science, Information Technology, Engineering or a related technical field and at least 7 years of related experience.
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.
Ability to design and communicate enterprise information architecture at conceptual, logical, and physical levels.
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.

Nice To Haves

Advanced degree preferred.
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.

Responsibilities

Analyze data processes, applications, and source data to understand dependencies, anomalies, and implicit business rules.
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.
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.
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.
Implement processes and systems to monitor data quality, ensuring production data is accurate, reliable, and available.
Identify, design, and implement internal process improvements, including automation of manual processes, optimization of data delivery, and redesign of infrastructure.
Participate in the development of future-state data architecture standards, guidelines, and principles.