Top Senior Data & Analytics Jobs

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Leads the design, development, optimization, and maintenance of scalable ETL pipelines and cloud-based data platforms. Builds Python data-processing applications, engineers ingestion and transformation workflows, improves data quality and reliability, and troubleshoots complex production issues. Collaborates with engineering teams and mission stakeholders, supports testing and deployment, modernizes data workflows, documents architectures, and guides best practices in an Agile environment.

Architect and scale high-throughput infrastructure for billions of multimodal video clips, including storage, indexing, retrieval, observability, workload balancing, versioning, lineage, and dataset exploration tools. Optimize distributed compute and cloud systems for reliability, latency, throughput, and cost. Support VLM integration into data pipelines and collaborate with research and ML systems teams. Staff candidates independently define technical direction and architecture.

Own the strategy and architecture for a scalable, multi-cloud data platform across Revv and AirPro. Integrate Azure SQL Server data with AWS, Fivetran, dbt, and Snowflake pipelines; build trusted dbt models; connect Salesforce and ERP systems; and establish governance, quality, ownership, and compliance foundations. Partner with analytics, finance, sales, product, and engineering leadership to translate business needs into reliable data assets and customer-facing products.

Leads data engineering projects from technical design through completion. Designs and optimizes data architectures, pipelines, warehouses, and reusable data assets; develops ETL processes and integrations with SAP systems; performs data validation, testing, troubleshooting, and code reviews; supports data governance and master data management; collaborates with data scientists; and communicates analytical findings through reports, presentations, and visualizations.

Designs and optimizes scalable data pipelines, services, and models using Python and SQL. Ensures data quality, reliability, and performance across large-scale storage and processing systems. Collaborates with analysts, data scientists, engineers, and cross-functional teams to deliver actionable data solutions. Integrates LLM and agent-based capabilities into data platforms and supports clinical, business intelligence, and operational needs.

Provides strategic leadership for insights, analytics, forecasting, and AI-enabled decision support. Develops performance metrics, dashboards, forecasting models, scenarios, and actionable recommendations for senior stakeholders. Evaluates and scales digital and AI tools, ensures responsible data governance, coaches teams, manages external partners, and drives cross-functional adoption of evidence-based decision-making and continuous improvement.

The Senior Data Analyst develops advanced SQL analyses, maintains Tableau and Hex dashboards, validates warehouse metrics, and translates data into actionable recommendations for executives, funders, government audiences, and community partners. The role emphasizes data storytelling, reporting standards, secure PII-aware delivery, stakeholder collaboration, and business impact evaluation. It also offers growth into AI-assisted analytics, custom visualization, and data-to-report automation.

Serve as the bridge between developers, functional analysts, and business stakeholders. Configure Adobe Experience Platform and Customer Journey Analytics data connections, data views, metrics, and dimensions. Define business requirements, build dashboards and reporting solutions, manage project workflows in Jira, and translate technical configurations and data insights for technical and non-technical audiences. The role requires strong analytics experience and may involve Adobe Journey Optimizer, cloud analytics platforms, and consulting experience.

Build and run an enterprise data governance program: create policies, standards, and controls; establish data use and de-identification programs (HIPAA-compliant); define ownership and stewardship; measure and remediate data quality; maintain governance artifacts; align cross-functionally; report and drive adoption and training.