Top Hybrid Data & Analytics Jobs

Conduct external marketing research and data storytelling, transforming complex datasets into defensible narratives for press, customers, and industry audiences. Serve as an approval gate for marketing claims, ensuring data is substantiated before legal review. Independently manage research projects, coordinate external partners, and collaborate with Legal and product marketing. Apply industry expertise, analytical judgment, SQL, Python or R, BI tools, and AI tools to produce rigorous published insights.

Maintains and governs SAP enterprise master data, including material masters, BOMs, routings, production versions, PIRs, source lists, and variant configurations. Manages SAP MDG workflows, validations, approvals, and data-quality rules. Conducts audits, resolves root causes, tracks governance KPIs, supports product launches, improves processes, and trains cross-functional teams on master-data standards and procedures.

Conduct quantitative research with senior researchers on trading strategies and models. Develop statistical models, investigate signals, optimize strategies, and analyze large-scale datasets using advanced computational resources. Collaborate in a rigorous academic environment, present complex ideas, and solve challenging real-world problems.

The Learning and Impact Manager will implement SPAA’s monitoring, evaluation, and learning systems; develop measurement frameworks; analyze portfolio and ecosystem data; facilitate learning reviews; and translate evidence into strategic recommendations, reports, funder engagement, and field-building content. The role partners across operations and programs to support adaptive management, grantmaking insight, impact reporting, and convenings. It requires strong synthesis, facilitation, organizational learning, and impact measurement skills, with approximately 25% domestic or international travel.

Own marketing analytics reporting and business reviews supporting go-to-market investment decisions. Translate business questions into actionable analyses, partner with marketing, sales, finance, product, customer success, and analytics engineering teams, and improve dashboards and reporting tools. Analyze campaign performance and business trends, contribute to data modeling, and independently manage analytics workstreams using SQL, Python, BigQuery, dbt, and Looker.

Builds and maintains Azure-based data infrastructure, including scalable ingestion pipelines, Databricks processing workflows, governance controls, data quality validation, and curated datasets for analytics and reporting. The role uses Python, PySpark, SQL, Azure Data Factory, Azure Functions, Logic Apps, and Azure Data Lake Storage Gen2, while partnering with IT, Finance, Operations, and Digital stakeholders. It is hybrid and requires several days in the Alexandria, Virginia office.

The Data Analyst will lead end-to-end analytics, develop metrics and dashboards, automate reporting and data-quality checks, and build lightweight scoring, classification, similarity, and risk-adjustment models. The role partners with Sales, Operations, Product, and leadership to answer ambiguous business questions, standardize reporting, and influence priorities. Strong SQL and Python skills are required, along with dashboard development and stakeholder communication. Healthcare data, dbt, Snowflake, and engineering collaboration experience are preferred.

Design and maintain enterprise business intelligence platforms, including dimensional models, semantic layers, data warehouses, Power BI solutions, and Microsoft Fabric integrations. Optimize SQL, DAX, refresh processes, and semantic model performance. Establish BI governance, security, deployment pipelines, source control, testing, monitoring, and documentation. Lead complex initiatives, evaluate emerging technologies, and translate business needs into scalable solutions while communicating technical recommendations and risks to diverse stakeholders.

The role involves designing and managing data pipelines, developing canonical datasets, and ensuring data integration and compliance. Collaborate across teams to meet data needs and maintain data architecture.

Owns the data transformation layer powering agency reporting. Builds reliable analytical models, dimensional schemas, reporting architecture, monitoring, and alerting. Translates government and customer reporting requirements into reusable data models and tools, supports embedded analytics and recurring imports, and ensures authorization scoping prevents cross-organization data exposure. Partners across product and implementation teams while establishing data engineering practices and documentation.