Top Senior Data & Analytics Jobs

Build and operate streaming and batch data pipelines for energy-storage telemetry. Design reliable, query-ready time-series and event datasets using open table formats, orchestration frameworks, AWS, and infrastructure-as-code. Lead technical designs, partner with field operations and engineering teams, support on-call operations, and mentor engineers while improving data reliability and engineering practices.

Own Serval’s complete data function as its first dedicated data hire. Build a unified warehouse, ETL/ELT pipelines, data models, quality and governance systems, documentation, and self-serve analytics. Partner with technical and business teams to define trusted metrics, consolidate product and SaaS data, and create AI-powered internal data tools. Select the data stack, establish engineering standards, and support scalable, cost-conscious infrastructure.

Leads College Board’s Operations analytics strategy and team, developing forecasting, predictive modeling, optimization, cost assessment, and decision-support capabilities. Partners with senior leaders across Operations, Product, Program, Technology, and Finance to improve execution, customer experience, efficiency, and cost savings. Responsibilities include setting technical direction, embedding analytics into operational decisions, presenting insights, hiring and coaching analytics staff, and building future AI and data-infrastructure capabilities.

Lead design and delivery of Snowflake-based data solutions, data models, ETL pipelines and data warehouse applications. Provide architecture direction, drive technology transformation and POCs, evangelize modern data architecture, collaborate with global stakeholders and Agile teams, and apply advanced analytics (ML/AI/GenAI) while ensuring data security and PII management.

Design, build, and maintain Databricks-based data platform and ETL/ELT pipelines. Collaborate with stakeholders, implement data quality checks and testing, monitor pipeline performance, document architecture, and coordinate cross-functional teams.

Manage, clean, validate, and analyze quantitative and survey data using SAS, R, and SPSS. Generate reports, data retrievals, summary outputs, surveillance reports, and presentation graphics. Review programs, error logs, variable definitions, weights, and data quality; document corrections and ensure protocol compliance. Merge data from multiple sources, support stakeholder reporting, and assist with data management processes and protocols.

Leads cross-functional data management and analytics projects for pharmaceutical and life sciences clients. Responsibilities include data warehouse development, ETL and schema design, data analysis, process optimization, client communication, project management, business development, and mentoring junior staff. The role also provides strategic guidance, develops reporting solutions, manages timelines and resources, and contributes to internal data platform initiatives.

Administer, optimize, and secure enterprise Microsoft SQL Server environments. Lead performance tuning, high availability, disaster recovery, monitoring, automation, upgrades, patching, and migrations. Support reporting and analytics workloads using SSRS and Qlik, maintain documentation and operational processes, collaborate with application and infrastructure teams, mentor junior engineers, and participate in on-call support.

Analyze credit, fraud, pricing, and portfolio performance using internal and external data. Cleanse and validate data, monitor predictive models and risk strategies, develop statistical models, identify trends and data-quality issues, and communicate actionable insights to leadership. Collaborate cross-functionally with product, engineering, compliance, and enterprise risk teams to support strategic decisions, improve profitability, and manage B2B credit risk.

Own healthcare analytics data methodology, including episode grouping, provider attribution, cost standardization, quality measures, reference data, pipelines, QA, and production delivery. Translate feature requests into specifications and validated analytics. Reconcile results, establish recurring data-quality coverage, investigate failures, and collaborate with engineering, data science, product, clinical informatics, and customer teams. Advanced SQL and healthcare claims expertise are central to ensuring accurate, defensible customer metrics.