Top Data & Analytics Jobs in Baltimore, MD

Lead statistical analysis for clinical studies in oncology and cardiology, provide project leadership, and mentor junior biostatisticians while ensuring regulatory compliance.

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Lead ownership of client billing data and infrastructure for Finance: run reconciliations, investigate billing issues, build BI dashboards, automate workflows with SQL/Snowflake, monitor revenue leakage, and support Billing 2.0 UAT while training Finance on reporting and Looker standards.

Manage analytical service operations by overseeing a team, designing software for data mining, developing statistical models, and generating insights for marketing outcomes. Provide training and mentorship to direct reports.

Gather and document business and functional requirements, analyze processes, recommend improvements, produce BRDs/FRDs/user stories and process maps, support solution design, UAT, testing and deployment, produce reports/dashboards, monitor project progress, and support change management and stakeholder communication.

Deploy and manage Open Metadata across cloud or on-premises environments using Kubernetes, Helm, Terraform, and Docker. Build data-source connectors and metadata ingestion workflows, administer metadata databases and Elasticsearch, and maintain security, monitoring, scalability, and disaster recovery. Support data governance, troubleshoot lineage and connectivity issues, document operations, train users, and collaborate with data stewards and engineering teams. The role requires three years of relevant experience and an active TS/SCI clearance with polygraph eligibility.

Develop data visualizations, interactive dashboards, and data pipelines to transform complex structured and unstructured data into actionable insights. Analyze data quality, types, structures, and relationships; use Python, R, or SQL for data manipulation and analysis. Collaborate with stakeholders to define reporting needs, support database-driven applications, and communicate findings. The role also involves mentoring team members, guiding algorithm and system development, and supporting defense and government clients.

Data Engineer supporting a United States Air Force program. Responsibilities include designing ETL pipelines, data warehouses and lakes, APIs, data quality controls, metadata and lineage practices, analytical datasets, security controls, automated deployments, monitoring, troubleshooting, and technical documentation. The role collaborates with government stakeholders, mission partners, technical teams, and data scientists while maintaining compliance with approved architectures, governance standards, and security boundaries.

Administers enterprise database environments for a U.S. Air Force program, including installation, configuration, monitoring, performance tuning, backups, recovery, permissions, patching, security controls, documentation, and incident support. Supports data warehouse and data lake environments, ETL integrations, reporting platforms, data quality, DevSecOps deployments, RMF assessments, ATO activities, continuity planning, and recovery testing. Coordinates database operations and outages with application, infrastructure, cybersecurity, and service desk teams.

Designs, deploys, and governs enterprise data platforms across classified and unclassified environments. Responsibilities include building data warehouses and lakes, developing automated ETL pipelines and secure APIs, implementing data governance and metadata tagging, monitoring data quality, managing access controls, and supporting secure cloud and hybrid systems. The role requires production-scale data engineering experience in controlled or federal environments and may support DoD classified systems and DevSecOps platforms.

Designs and builds scalable analytics data models, consumption layers, and transformation pipelines supporting BI, reporting, and AI applications. Partners with stakeholders to translate business needs into technical requirements, improves data architecture and quality, applies software engineering best practices, evaluates data tools, troubleshoots issues, and optimizes data infrastructure and workflows.