Top Hybrid Data & Analytics Jobs in Boston, MA

Leads data transformation and governance for the Personal Lines Product, Pricing, and Underwriting domain. Develops data roadmaps, translates plans into epics, guides modernization onto a new technology stack, governs data policies and regulatory protections, and coordinates stakeholders across business and technical teams. The role requires strong insurance data expertise, knowledge of complex data models and warehousing, Agile collaboration, and experience with platforms such as Guidewire PolicyCenter.

Leads WHOOP’s data governance function, defining strategy, operating models, ownership, data contracts, event taxonomy, cataloging, access policies, quality standards, and privacy compliance. Builds and coaches a senior team while partnering across engineering, analytics, product, legal, GRC, and InfoSec. Establishes governance frameworks, automated quality monitoring, access controls, compliance practices, and metrics, while embedding standards into technical workflows and advancing AI-powered governance.

Develop, implement, govern, and integrate enterprise ontologies and data models. Harmonize linked data, establish semantic and data-standardization guidelines, support knowledge organization and machine-learning integrations, and advocate semantic technology across Finance and Enterprise teams. Use W3C ontology standards and specialized tools to test, troubleshoot, and maintain models while collaborating with Technology, Product, and Machine Learning stakeholders.

Analyze and visualize large customer datasets, build production-grade data models and scalable data products, automate manual analytical workflows, and create AI-assisted, conversational, and self-service analytics experiences. Translate ambiguous stakeholder needs into actionable solutions while collaborating across teams and experimenting with generative AI and modern data technologies.

Design and execute advanced statistical and data analyses for client projects, using tools such as R, Tableau, SAS, VBA, and Excel. Translate data into actionable insights, communicate findings to clients and internal teams, collaborate on solution implementation, and take ownership of project deliverables.

Supports client-facing diligence analytics projects by gathering, integrating, and analyzing data; developing data-driven insights; creating dashboards and reports; conducting research and data mining; contributing to algorithm development; and preparing presentations. The intern uses Alteryx and Power BI, collaborates with advisory teams, and gains exposure to cloud platforms, data architecture, data science, and AI applications.

Leads the data engineering team, including hiring, coaching, technical direction, platform architecture, data pipelines, Snowflake warehousing, governance, quality, observability, and incident support. Partners with Data Science, Analytics, Clinical Operations, Biostatistics, and Business Development to deliver trusted data products for clinical operations, asset evaluation, reporting, and machine learning. Oversees budgeting, resourcing, documentation, technical programs, and responsible use of AI engineering tools.

Design, build, and operate high-throughput data pipelines supporting billing and cost attribution. Drive Spark optimization, latency reduction, sharding, cross-datacenter reliability, and new billing capabilities. Investigate billing accuracy issues with Finance and Product teams, participate in on-call rotation, and improve engineering standards and operational excellence.

Supports Fund Management through regulatory communications, cost allocation, operational reporting, and analytics. Develops and maintains Access, Excel, SQL, and enterprise reporting solutions; integrates data across systems; improves data quality and workflows; and supports testing, validation, deployment, automation, and AI-enabled process enhancements. The role also serves as a technical subject matter expert and coordinates regulatory reporting while driving efficiency and cost savings.

Analyzes large-scale datasets to generate strategic insights, design and evaluate experiments, identify trends, and improve business performance. Builds scalable reporting frameworks and dashboards, translates complex analyses into compelling narratives, and partners with cross-functional teams to support strategic initiatives and data-driven decisions across casino products and markets.