Top Hybrid Senior Level Data & Analytics Jobs in Boston, MA

Develops and governs financial data solutions for Liberty Mutual’s Finance Data Platform. Responsibilities include analyzing source data, defining transformation and downstream reporting requirements, applying metadata and lineage principles, prototyping coded solutions, testing, enabling self-service data consumption, defining business metrics, mentoring junior staff, and collaborating across finance, business, and technology teams. The role also identifies opportunities to use generative AI for data discovery, documentation, lineage summarization, prompt engineering, and code generation.

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.

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.

Leads the design, development, and ownership of scalable data systems, ELT pipelines, data models, and Snowflake architectures supporting analytics, experimentation, machine learning, and business decisions. Partners cross-functionally to deliver reliable, governed data solutions; improves quality, observability, testing, documentation, and operational practices; mentors engineers; and shapes technical direction through automation, technology evaluation, and AI-enabled engineering.

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.

Lead commercial analytics to diagnose sell-through, pricing, promotions, assortment, and retailer execution. Build causal and incrementality measurement, design test-and-learn, support elasticity and margin analyses, and deliver executive-ready insights and frameworks to optimize revenue, margin, and promotional efficiency across channels and retailers.

Support Business Banking with analytics, reporting, dashboards, and automation to drive deposit acquisition, onboarding, funded balance growth, and retention. Produce forecasting and pipeline reporting, provide deposit strategy and profitability insights, enable go-to-market and campaign analysis, and standardize onboarding and data processes to improve efficiency and data quality.

Build and productize externally consumable data assets from concept to delivery. Define schema, governance, SLAs, documentation, and delivery (Snowflake share, SFTP, API). Partner cross-functionally to productionalize datasets, monitor quality, iterate on customer feedback, and create repeatable productization frameworks.

Lead enterprise healthcare data modeling: design and maintain conceptual/logical/physical models, integrate varied clinical sources, implement common data model transformations, monitor data quality, and collaborate with clinicians, data scientists, and engineers.