Top Data & Analytics Jobs in Huntsville, AL

Develops data pipelines, analysis tools, dashboards, reports, spreadsheets, and customer briefings for military test events. Operates live display and analysis systems, collects and interprets live and post-mission test data, and provides real-time operational insights during field deployments of up to 25%.

Provides analytical and lifecycle support for U.S. Navy electromagnetic warfare, decoy, and SEWIP systems. Applies operations research, data science, logistics analysis, reliability and maintainability engineering, mathematical modeling, simulation, optimization, and what-if analysis to improve system readiness, operational availability, resource allocation, and lifecycle performance. Develops performance metrics, evaluates alternatives, analyzes complex datasets, and delivers recommendations and decision-support products to technical and program leadership.

Design and maintain petabyte-scale data architectures and ETL pipelines; enable automated MLOps workflows; lead teams building ML systems and backend services; implement CI/CD and DevSecOps practices; integrate components, write maintainable Python code, and produce technical documentation while collaborating with cross-functional teams.

Produce and maintain reports and dashboards, deliver analytical solutions and ad hoc projects under team leader direction. Build domain knowledge, learn bank data and platforms (BIC, Tableau), ensure data compliance, and collaborate with stakeholders.

Support Feed and Logistics by creating data analysis tools, extracting KPIs from major systems, building QlikView dashboards and reports, performing statistical analysis, and liaising with business stakeholders to meet reporting needs.

Lead customer engagements to design, build, and optimize Databricks architectures for data engineering, analytics, and machine learning. Develop scalable ETL/ELT pipelines, integrate with cloud platforms, advise on governance/security, productionalize ML workflows, troubleshoot operational issues, and deliver consulting and managed services with documentation and best practices.

Lead complex data mining, statistical and trend analyses; produce monthly contract reports; integrate operations research methods for managers; prepare training materials; perform quality control; transform structured and unstructured data into actionable analytic products and recommendations for DoD operational decision makers.

Designs scalable ETL/ELT pipelines, analytics-ready data models, and executive dashboards using Python, SQL, Tableau, Power BI, and cloud tools. Performs data mining, modeling, validation, documentation, and reporting while collaborating with technical and business stakeholders. Provides mentorship and technical guidance on analytics engineering, workflow automation, and data best practices. The role is fully remote, requires U.S. citizenship, and requires or permits obtaining Public Trust and Top Secret eligibility.

The Business Data Analyst elicits and documents business, functional, and data requirements for cloud-based data products. Responsibilities include source-to-target mapping, stakeholder workshops, data governance coordination, user stories, acceptance criteria, testing and UAT support, metadata documentation, Agile artifacts, and audit-ready reporting documentation. The analyst bridges federal stakeholders with data engineering, architecture, analytics, and governance teams while supporting compliant data modernization and AI/ML initiatives.

Designs and maintains enterprise data architecture, including data lakes, warehouses, integrations, migrations, orchestration, and data management protocols. Partners with architects, DBAs, BI teams, product leaders, and business stakeholders to support analytics and data science. Requires deep experience with cloud data environments, Azure SQL, Microsoft Fabric, Spark, Hadoop, PySpark, SparkSQL, security protocols, and enterprise data integrations.

Leads enterprise data strategy, lakehouse delivery, data products, and data and AI governance. Establishes platform standards, data ownership, quality, lineage, lifecycle, privacy, and regulatory controls. Builds governance operating models for AI development and monitoring, manages platform teams and partners, oversees external data acquisition, and reports risk, maturity, and compliance to executives, clients, and auditors.