Data Architect Supervisor

Zeiders Enterprises, Inc.•Woodbridge, VA

About The Position

The Data Architect Supervisor is a full-time employee of Zeiders Enterprises, Inc. responsible for designing, developing, managing, and continuously improving the organization's enterprise data architecture and data technology environment. This hands-on role establishes a scalable, secure, and reliable data foundation for business operations, reporting, analytics, automation, and emerging AI capabilities. The position translates business requirements into practical data solutions and develops the standards, processes, and technologies needed to support a data-driven organization. The supervisor works with IT, business leaders, cybersecurity, finance, human resources, and other stakeholders to ensure data is accessible, accurate, secure, governed, and actionable. Responsibilities span data architecture and engineering, integration, governance and quality, government contracting and security, business intelligence and AI, project and stakeholder management, and supervision of data analysts, engineers, and other assigned support staff. The role balances strategic leadership with hands-on technical execution.

Requirements

Bachelor's degree in computer science, Information Systems, Data Science, Engineering, or a related field.
At least 8 years of progressive experience in data architecture, data engineering, database management, business intelligence, or related technology disciplines.
Demonstrated hands-on experience managing data technologies and enterprise data environments.
Experience designing and implementing data architectures, data warehouses, data integration solutions, and data pipelines.
Experience with relational databases, Python, and SQL.
Experience with cloud-based data technologies and platforms.
Experience developing ETL/ELT processes and integrating data from multiple systems.
Experience with data orchestration tools such as Kafka, FiveTran, and dbt.
Experience with data modeling, data governance, data quality, and metadata management.
Experience supporting a government contractor or federal government organization.
Experience working with CUI, NIST SP 800-171, CMMC, FedRAMP, or other government cybersecurity requirements.
Strong analytical, problem-solving, communication, and stakeholder management skills.
Ability to establish a practical data technology strategy while remaining hands-on with architecture, engineering, and problem resolution.
Ability to understand how data flows across applications, business processes, programs, and organizational functions.
Strong understanding of data architecture, modeling, integration, quality, governance, security, and lifecycle management.
Understanding of government contracting security, compliance, contractual, and data protection requirements.
Ability to lead a technical team and foster accountability, collaboration, knowledge sharing, and professional development.
Ability to translate business requirements into practical data solutions and communicate technical concepts effectively to business leaders.

Nice To Haves

Experience with Microsoft Fabric, Azure Data Factory, Azure SQL, SQL Server, Power BI, Databricks, or similar technologies.
Experience supporting enterprise data warehouses, data lakes, or Lakehouse architecture.
Experience with APIs, integration platforms, and modern data architectures.
Experience supporting AI, machine learning, or intelligent automation initiatives.
Experience establishing or maturing an enterprise data governance program.

Responsibilities

Develop and maintain the enterprise data architecture, including data sources, databases, warehouses, lakes, integrations, APIs, and reporting platforms.
Design scalable data solutions for operational systems, business intelligence, analytics, automation, and AI initiatives.
Evaluate, select, implement, and manage data technologies and platforms; establish architecture standards, principles, patterns, and technology roadmaps.
Assess the current data environment and develop a multi-year modernization and optimization roadmap.
Identify opportunities to consolidate, integrate, automate, and improve the data environment.
Perform hands-on data engineering, database administration, integration, and architecture work as appropriate.
Monitor data technology performance, capacity, availability, and reliability.
Design and implement data pipelines and ETL/ELT processes integrating enterprise applications and external sources.
Develop and maintain data models for operational reporting, analytics, and business intelligence.
Establish integration patterns connecting applications, databases, cloud services, and other data sources.
Ensure pipelines are reliable, documented, monitored, and appropriately secured; troubleshoot complex data, integration, and performance issues.
Support data warehouses, data marts, and analytical environments.
Establish and maintain data governance practices, standards, policies, and procedures.
Define data ownership, stewardship, classification, retention, and lifecycle requirements.
Develop processes to monitor and improve data quality, completeness, accuracy, consistency, and timeliness.
Maintain data dictionaries, metadata repositories, business glossaries, and data lineage documentation.
Work with business stakeholders to identify authoritative data sources and establish common data definitions; promote data as an enterprise asset and establish appropriate controls on its use.
Align data architecture and solutions with applicable government contracting, cybersecurity, privacy, and compliance requirements.
Support protection of CUI, PII, and other sensitive organizational information.
Collaborate with cybersecurity and infrastructure teams on access controls, encryption, monitoring, logging, and data protection mechanisms.
Support contractual, regulatory, and organizational compliance, including applicable NIST-based security controls.
Ensure appropriate data segregation, access, retention, and handling in government and commercial environments.
Participate in security assessments, risk reviews, technology evaluations, and compliance activities as required.
Partner with business and technology teams to establish a reliable data foundation for enterprise reporting and analytics.
Support dashboards, management reporting, operational metrics, and executive-level analytics.
Enable data solutions for automation, machine learning, and AI initiatives; establish standards for data preparation and accessibility for analytics and AI use cases.
Identify opportunities to use data to improve operational efficiency, customer experience, program performance, and decision-making.
Work with executives, program managers, business leaders, and technical teams to understand requirements and translate them into technology solutions.
Lead or contribute to data-related technology projects and strategic initiatives; develop project plans, technical requirements, architecture documentation, and implementation approaches.
Communicate complex technical concepts to technical and non-technical stakeholders.
Manage relationships with data technology vendors and service providers and participate in technology planning, budgeting, procurement, and vendor evaluations.
Directly supervise data analysts, engineers, and other support staff as assigned.
Manage employee selection, onboarding, coaching, performance management, disciplinary actions, and professional development.
Lead and oversee the data management team's operational activities.
Lead, mentor, and develop data professionals; establish team priorities, objectives, performance expectations, and professional development plans.
Assign work based on organizational priorities, technical capabilities, and project requirements.
Set standards for documentation, development, testing, deployment, and operational support.
Promote collaboration between the data team and other IT and business functions, fostering accountability, knowledge sharing, and continuous development.
Balance strategic responsibilities with hands-on technical execution and carry initiatives from strategy and architecture through implementation, operationalization, and continuous improvement.