Top Data & Analytics Jobs in Washington, DC

Analyze aggregated ChatGPT usage data to identify geographic and thematic trends in AI adoption. Build dashboards, recurring reports, and analytical frameworks; combine internal and public datasets; evaluate data limitations and privacy requirements; and translate findings into clear narratives for policymakers, communications teams, stakeholders, and the public. Collaborate cross-functionally to support policy, public affairs, stakeholder engagement, and strategic communications.

Build and maintain Tableau dashboards, Snowflake Streamlit applications, and dbt models for the Developer Led Growth team. Analyze experiments and initiatives, create measurement frameworks, identify data gaps and opportunities, and translate findings into actionable recommendations. Own analytics prioritization, communicate with technical and executive stakeholders, and use SQL, AI tools, and automation to scale analytical processes.

Build and operate a Kubernetes-based data platform spanning lakehouse storage, ingestion, transformation, entity resolution, lineage, and data quality. Develop batch and streaming pipelines across heterogeneous and disconnected environments, create full-stack tools for subject matter experts, and expose documented APIs for applications and AI agents. Collaborate with product, AI/ML, infrastructure, mission partners, and field teams while mentoring engineers and supporting secure, air-gapped deployments.

Leads enterprise data analytics, business intelligence, reporting, governance, and performance management initiatives. Develops data strategies, integrates information from enterprise systems, builds executive dashboards and KPI reporting, performs forecasting and trend analysis, and identifies automation and AI opportunities. Provides strategic insights for leadership decisions and manages data management and reporting teams in DHS or DoD environments.

Build, test, maintain, and improve data pipelines, data models, and ETL/ELT processes supporting firm analytics and reporting. Ensure data accuracy, reliability, governance, monitoring, and observability while troubleshooting quality issues. Collaborate with data engineers, product owners, and business stakeholders; document pipelines and schemas; participate in Agile ceremonies; and use Git for code changes. The role also supports data modernization and may involve cloud data platforms, orchestration tools, and AI/LLM technologies.

Develop complex Power BI dashboards, reports, and semantic models for government enterprise reporting and data modernization initiatives. Responsibilities include DAX development, data modeling, visualization, performance tuning, OBIEE-to-Power BI migration, Power Automate workflow automation, ETL and data integration, refresh strategies, Agile delivery, and technical documentation. The role is fully onsite in Washington, DC, requires 15+ years of experience, U.S. work authorization, and the ability to obtain and maintain a Public Trust clearance.

The Director of Quantitative Analysis leads IWPR’s quantitative research strategy, oversees research staff, manages project sequencing and rapid-response research, and ensures analytical rigor and replicability. The role conducts and advises on multivariate and survey research, integrates racial equity and intersectional frameworks, translates findings into policy publications, and collaborates with communications, policy, and advancement teams. The director also represents IWPR externally, develops staff capabilities, and manages research capacity, priorities, deadlines, and performance.

Leads commercial analytics strategy, revenue performance reporting, metrics governance, data reliability, executive decision support, and AI-enabled commercial intelligence. Builds standardized reporting architecture across Salesforce, Power BI, finance, billing, and marketing systems; analyzes sales and revenue motions; enables governed self-service analytics; and partners with RevOps, Data Engineering, Data Science, Finance, and commercial leadership to improve forecasting, sales productivity, and business outcomes.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Leads PBM data integration programs involving claims, eligibility, accumulator, and encounter data. Profiles, maps, validates, and reconciles data using advanced SQL; investigates production issues; designs scalable healthcare integrations; coordinates vendors, engineering, testing, UAT, migration, and deployment readiness; and manages timelines, risks, dependencies, and executive reporting. The role also maintains documentation and operational procedures while directing day-to-day integration work.