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

Conduct quantitative research using surveys, experiments, and advanced statistical methods for healthcare and life sciences client engagements. Analyze data, develop insights and visualizations, support rigorous research design, and translate findings into actionable recommendations. Collaborate with consulting teams and global delivery groups while ensuring research quality and communicating complex concepts to technical and non-technical audiences.

Lead and mentor a product analytics team that diagnoses product performance, evaluates launches, defines trusted KPIs, and drives strategic decisions. Oversee rigorous experimentation, causal inference, cohort analysis, metric governance, and telemetry standards. Partner with Product, Engineering, Marketing, Growth, Design, and Finance leaders to translate analysis into recommendations. Promote AI-native workflows using tools such as Claude Code while ensuring analytical quality, code validation, and clear executive communication.

Supports client portal data, reporting, dashboards, and visualizations for investment data. Translates business needs into requirements, mappings, user stories, and acceptance criteria; validates Power BI and Snowflake data; performs reconciliation and root-cause analysis; supports production incidents, releases, and documentation; and coordinates an offshore support partner across priorities, testing, issue resolution, and operational readiness.

Leads commercial data governance strategy, operating models, policies, standards, ownership, stewardship, metadata, lineage, and data-quality programs. Partners with cross-functional stakeholders to improve data integrity, accessibility, privacy, compliance, and responsible use. Oversees governance councils, catalogs, glossaries, issue remediation, performance metrics, tool adoption, training, and change management across a regulated biotechnology commercial organization.

Manages research data acquisition, licensing, contracts, vendor relationships, governance, and compliance at Harvard Kennedy School. Leads data safety staff, evaluates contractual and privacy risks, develops data management plans and SOPs, oversees access and disposition workflows, and advises researchers and institutional stakeholders on data-use obligations, regulatory requirements, and risk mitigation.

Own enterprise client reporting and analytics for influencer marketing campaigns. Analyze creator, content, audience, platform, and campaign data to identify performance drivers and translate findings into actionable recommendations. Build client-facing reports, executive presentations, scalable dbt and BigQuery data models, standardized metrics, reusable data assets, and quality checks. Partner with Client Success, Services, Sales, data engineering, and product teams to support client value, renewals, expansion, and scalable analytics frameworks.

Leads and develops a data engineering chapter while remaining hands-on in an agile squad. Responsibilities include coaching specialists, defining future skills, designing scalable data pipelines and infrastructure, establishing engineering standards, partnering on architecture, improving data quality, supporting governance and privacy, and collaborating with data scientists, analysts, and business stakeholders. The role also supports performance management, workforce planning, recruitment, and talent development.

Designs, develops, and maintains enterprise ETL solutions using Informatica, SQL, cloud platforms, and data warehousing practices. Builds scalable data pipelines, integrates Salesforce and other enterprise systems, monitors data quality and performance, troubleshoots issues, and supports cloud modernization. Collaborates with business and technology teams in a regulated financial services environment while contributing to technical standards, documentation, governance, and mentoring junior developers.

Build and maintain scalable data pipelines, models, and cloud infrastructure supporting business performance analysis. Collaborate with data scientists, ML engineers, product teams, and business stakeholders to translate requirements into technical solutions. Develop self-service data tooling, optimize Snowflake and AWS environments for scale and cost, and support deployment through CI/CD and DevOps practices. The role also involves data integration, modeling, documentation, and ensuring data integrity across global entities.

Principal technical leader responsible for architecting and operating relational data marts, dbt data products, semantic layers, and shared data services. Builds scalable solutions in Databricks, Delta Lake, and Spark; owns data products through testing, deployment, documentation, monitoring, and support. Establishes governed metrics, data quality, lineage, observability, and access controls while partnering cross-functionally to resolve ambiguity. Mentors engineers, improves reusable frameworks, and supports BI, data science, and AI use cases.