Data Architect

Custom Software Systems Inc.•Washington, DC

3d•$150,000 - $175,000

About The Position

Custom Software Systems, Inc. (CSS) is seeking a Senior Data Architect/Engineer to support a critical data modernization initiative for our client. The selected candidate will play a key role in transforming legacy data environments into scalable, cloud-enabled data platforms that improve data accessibility, security, and analytical capabilities supporting research, forecasting, and data-driven decision-making. The Senior Data Architect/Engineer will be responsible for designing and implementing enterprise data architectures, developing robust data integration and pipeline solutions, and optimizing data delivery processes across complex business and analytical environments. This role requires a hands-on technical leader with expertise in data modeling, database architecture, ETL/ELT development, cloud technologies, and data governance. The ideal candidate will collaborate closely with business stakeholders, researchers, and technical teams to deliver innovative, secure, and scalable data solutions that advance organizational objectives and support next-generation analytics initiatives.

Requirements

Minimum of ten (10) years’ experience developing data architecture and data engineering solutions at enterprise scale.
Minimum of five (5) years’ experience of Data Warehouse Architecture.
Minimum of five (5) years of experience with business intelligence technologies and solutions and leading teams on IT initiatives.
Minimum of one (1) year of experience with cloud platforms such as AWS and Azure.
Minimum of two (2) years’ experience working in an agile development environment.
Well-versed in data governance and management.
Strong background in various types of data infrastructure and services.
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.
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.
Solid understanding of software engineering principles, design methodologies, technology architecture, and best practices in IT industry.
US Citizenship

Nice To Haves

Solid understanding of software engineering principles, design methodologies, technology architecture, and best practices in IT industry.
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 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.
Collaborate with important stakeholders to establish an understanding of the organization’s vast processes and data.
Actively participate in process improvement initiatives and in setting best practice standards around Data Architecture, Metadata, Governance, Master Data Management, and Data Quality flows.