Data and AI Project Manager – Public Sector

Responsibilities

• Design and lead AI/ML and analytics solutions using best-in-class tools and platforms.
• Translate business challenges into actionable use cases and scalable data and AI products and services.
• Translate complex business challenges into actionable AI/ML and data solutions using platforms such as Snowflake, Databricks, Azure, AWS, and GCP.
• Lead client engagements from strategy through implementation, ensuring delivery excellence and measurable outcomes.
• Mentor and lead multidisciplinary teams including scientists, engineers, and consultants.
• Lead data warehouse and or lakehouse initiatives, including requirements definition, architecture, modeling, implementation, and production readiness.
• Design and implement scalable data warehouse patterns, including dimensional modeling (star/snowflake), curated data marts, semantic layers, and governed self-service analytics.
• Direct development of ETL/ELT pipelines, orchestration, and automation using modern tooling and engineering practices.
• Establish and mature data operationalization capabilities (e.g., performance tuning and cost optimization, release management and incident support processes, data quality rules and monitoring).
• Partner with client stakeholders to align warehousing work with data governance practices, access controls, and operational support models.

• Bachelor’s degree from an accredited college/university.
• Based on our contractual obligations, candidate must be located within the United States and US Citizen.
• Must be able to OBTAIN and MAINTAIN a Federal or DoD "PUBLIC TRUST".
• Minimum FIVE (5) years of experience delivering technical solutions and programs to public sector organizations.
• Experience in data and AI system development, with a proven ability to design scalable architectures and implement reliable models.
• Demonstrated experience leading data warehouse design and implementation, including data modeling, ingestion patterns, and production operations/ Strong knowledge of data warehousing concepts (e.g., dimensional modeling, analytics-ready data structures, CDC patterns, performance tuning, and cost controls for cloud platforms).
• Experience defining and implementing operating models for data platforms, including support processes, SLAs, monitoring, and continuous improvement.
• The knowledge and interest to remain current on emerging trends and techniques in the fields of data science and Artificial Intelligence.
• Strong communication skills to bridge technical and business worlds.