Top Senior Analysis Reporting Jobs in Washington, DC

The Senior Data Analyst will provide analytical support for military assistance programs, ensuring data integrity, visualizations, and trends analysis while collaborating closely with clients.

Develops and administers the organization’s information management system and plan, including governance, records management, lifecycle controls, data classification, security, privacy, data quality, compliance, and information architecture. Establishes policies, retention schedules, standards, documentation, and performance measures; supports information systems and audits; provides training and recommends continuous improvements. Requires onsite work in Albuquerque or Washington, DC, and a Department of Energy Q-level clearance or ability to obtain one.

Supports FAA implementation efforts by integrating and reconciling complex schedule, inventory, procurement, installation, and operational datasets. Builds repeatable data workflows, validates records, analyzes implementation risks and inventory gaps, prepares schedule updates, and develops dashboards and reports. The role documents data processes, investigates discrepancies, coordinates with schedulers and implementation stakeholders, and communicates progress, risks, and recommendations. Occasional client or company travel is required.

Leads performance measurement, KPI development, reporting, and data-driven decision support for federal programs. Analyzes data from databases, APIs, files, and Amazon S3 using Python and SQL; develops validation methods, dashboards, reports, and analytical narratives. Partners with engineering, product, UX, delivery, and government stakeholders; evaluates generative AI outputs; establishes data quality standards; mentors colleagues; and translates ambiguous client needs into practical recommendations.

Build and maintain dbt data models while migrating workflows from Alteryx, managing Dagster or Airflow pipelines, and supporting Redshift analytics. Partner with RevOps stakeholders to translate data into business insights, dashboards, KPIs, ABM initiatives, and customer health scoring. Explore LLMs, predictive analytics, and SageMaker machine learning for use cases such as churn prediction and lead scoring.

Leads the design, development, and delivery of dashboards, KPI frameworks, benchmark analyses, and reporting solutions. Integrates data from multiple systems, improves data quality, manages analytical standards, and translates business needs into actionable insights. Collaborates with cross-functional stakeholders, mentors analysts, reviews analytics products, supports program and portfolio reporting, and presents findings to executive and operational audiences.

Lead architecture and build scalable data pipelines and ETL for large datasets, optimize data models and DB structures, ensure data quality and governance, work with cloud platforms (Snowflake/AWS), mentor junior engineers, collaborate with data scientists and product teams, and apply geospatial and big-data technologies to deliver data-driven transportation solutions.

Lead data analytics initiatives supporting federal program performance management and leadership decision-making. Develop advanced analytics solutions, integrate data from SAM, FSMS, and SharePoint, create dashboards and performance metrics, ensure data accuracy and federal compliance, and provide insights to improve operational efficiency. The role requires on-site work in Washington, D.C., prior DHS or DoD experience, and extensive data analytics experience.

Owns the enterprise Data Lakehouse platform, including operations, configuration, integrations, monitoring, data validation, issue resolution, vendor management, releases, documentation, security controls, and lifecycle improvements. Translates stakeholder requirements into data solutions, coordinates cross-functional technology teams, guides platform initiatives, and ensures reliable support for business intelligence, reporting, analytics, and AI use cases.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.