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

Own high-impact revenue strategy analyses from problem framing through recommendations and decision support. Analyze marketplace performance, lead economics, monetization, retention, product adoption, and operating models using SQL, Python, financial modeling, and cross-functional data. Advise senior leaders, create executive- and board-ready materials, improve metric reliability, build reusable analytical frameworks, and influence decisions across Revenue, Product, Finance, and Operations.

Own commercial analytics for direct-to-consumer and marketplace channels, analyzing pricing, promotions, conversion funnels, merchandising, product mix, customer behavior, and channel economics. Build scalable reporting tools, support forecasting and planning, and translate transactional and behavioral data into recommendations for digital commerce leadership. Partner with Marketing Analytics and Analytics Engineering to connect acquisition trends with commercial outcomes and ensure trusted data.

Leads data engineering architecture and modeling initiatives, designing scalable pipelines, cloud data platforms, and reusable data solutions. Collaborates with engineering, product, analytics, and data science teams while mentoring developers and establishing common design patterns. Responsibilities include distributed data processing, relational and NoSQL systems, data security, observability, streaming pipelines, and operational excellence across platforms such as Databricks and Snowflake.

Designs and maintains SQL/Python data pipelines, warehouse models, and Power BI dashboards supporting finance planning, forecasting, budgeting, and book close. Owns solution architecture from requirements through deployment, validates data and systems, resolves process and reliability gaps, and manages multiple enhancements. Collaborates with business and technical stakeholders, mentors analysts, leads continuous improvement, documents solutions, trains users, and develops automated and AI-enabled efficiencies.

Leads quantitative and qualitative consumer research projects, brand-health tracking, audience measurement, and recurring insights programs. Analyzes surveys, behavioral metrics, syndicated data, and competitive intelligence to identify trends and business implications. Creates reports and presentations that translate findings into recommendations for marketing, brand, product, and customer experience teams. Manages survey platforms, research quality, vendors, timelines, and cross-functional stakeholder communication.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Lead and coach teams to design, code, test, and deliver software solutions driving digital transformation for tax clients. Translate requirements into technical specs, perform code reviews, troubleshoot complex issues, maintain client relationships, and promote innovation and systems thinking to validate outcomes and improve products.

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.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.