Enterprise Architect - Data Engineer

Arcfield•McLean, VA

About The Position

The Data Engineer supports the Sponsor’s mission by addressing complex data challenges and collaborating with cross-functional teams. The role develops innovative solutions to unusually complex technical problems and independently determines approaches to achieve desired results. The position designs and implements scalable data architectures for real-time processing and integration while minimizing cost and complexity. It develops data pipelines, data quality checks, filtering and processing algorithms, and new data flows that integrate external sources into Sponsor repositories using AWS methods and other options. The role also collaborates with Data Scientists, stakeholders, customers, and downstream data repositories to deliver prioritized data streams and optimized data flows. The Enterprise Architect aspect of the position provides technical leadership for enterprise information and data management practices. The role leads and participates in projects involving enterprise information architecture, information models and flows, data dictionaries, standards, and quality processes. It develops and maintains a logical Enterprise Information Architecture to support interoperability among transaction systems, document management systems, and information delivery systems. The position ensures compliance with applicable data stewardship, documentation, and security requirements. The projected compensation range is $125,133.53 to $217,582.94. Final compensation may vary based on factors including relevant experience, skills, location, education, certifications, and applicable contract labor categories. Benefits may include health and life insurance, paid time off, holiday pay, short- and long-term disability, retirement and savings, learning and development opportunities, wellness programs, and optional benefit elections, depending on position.

Requirements

Must possess and be able to maintain a TS/SCI clearance with polygraph.
Bachelor’s degree or equivalent experience in Computer Science, Information Systems, Engineering, Business, or a related scientific or technical discipline.
Experience level stated as BS 10–12 years, MS 8–10 years, or PhD 5–7 years.
Knowledge of and experience with the Sponsor’s environment.
Knowledge or experience with cloud-based data platforms such as AWS, GCP, or Azure.
Experience with system architecture and implementation of data solutions.
Experience with data engineering tools and techniques.
Experience programming in languages such as Python, Java, or C++.
Experience building data pipelines and integrating multiple data sources.
Experience working with challenging data sets and developing data filtering and processing algorithms.
Experience developing information architectures aligned with information lifecycle management.
Experience performing conceptual, logical, and physical data modeling using data modeling tools and database design techniques in complex, large-scale data environments.
Experience with transactional, dimensional, and object-oriented data modeling.
Experience developing scalable entity designs using advanced data structures such as tall tables.
Experience developing data models supporting time dependencies, historical tracking, simple and complex taxonomies, and multilingual data.
Experience creating data dictionaries, defining metadata management approaches, and establishing governance processes.
Experience developing and implementing IT architecture plans, Enterprise Information Architecture standards and guidelines, software development methodologies, and strategic plans.
Experience developing data sharing standards and architecting scalable service-oriented solutions.
Experience creating enterprise models incorporating master and reference data.
Solid understanding of transaction and information systems, data warehouses, data marts, ODS, MDM methodologies and life cycles, and ETL and MDM technologies.
Knowledge of application modeling languages and techniques such as UML, OML, entity relationship modeling, and DFDs, including the ability to create and read associated diagrams.

Nice To Haves

Familiarity with data visualization tools such as Tableau or Power BI.
Experience with machine learning frameworks such as TensorFlow or PyTorch.
Experience with data quality management and data validation techniques.
Ability to work with sensitive data sources and develop data anonymization algorithms.

Responsibilities

Design and implement scalable data architectures for real-time data processing and integration, minimizing cost and complexity while enabling the mission.
Collaborate with Data Scientists to deliver data streams of interest and prioritize data types for rapid analysis.
Develop data integration pipelines and maintain data quality checks.
Engage high-priority stakeholders to provide data-driven solutions.
Work with downstream data repositories to meet needs and optimize data flows.
Support customers unfamiliar with the data or data engineering concepts, including targeters and engineers.
Develop data filtering and processing algorithms.
Establish data flows integrating external sources into Sponsor repositories using AWS methods and other options.
Ensure compliance with data stewardship, documentation, and security requirements.
Provide technical leadership and direction in implementing enterprise information and data management practices.
Lead and participate in projects involving Enterprise Information Architecture, information models and flows, data dictionaries, data standards, and data quality processes and standards.
Develop and maintain the logical Enterprise Information Architecture to enable interoperability among transaction, document management, and information delivery systems.