Top Data & Analytics Jobs in Boston, MA

Supports retail space planning through data analytics, performance reporting, and store layout optimization. Creates and validates Power BI dashboards and reporting tools, analyzes space productivity, maps data, and identifies actionable opportunities. Collaborates with Space Planning and Commercial Solutions teams while maintaining merchandising standards. The co-op combines business analytics, retail strategy, database concepts, data visualization, and emerging AI tools in a hybrid environment.

Owns the roadmap and backlog for pricing tools, gathers requirements, and coordinates delivery with data science engineering teams. Develops and maintains Earnix and SAS pricing tools, analyzes premium mismatches, resolves defects, conducts quality control, and supports rating program rollouts. Uses Python, SAS, Excel, and related technologies for pricing analysis and tool development. Collaborates with cross-functional partners, presents results, and supports knowledge sharing and process improvement.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Leads a team of analytics professionals and owns client and product analytics strategy, dashboards, reporting, metrics governance, and self-service capabilities. Partners with Product, Engineering, Client Success, and account teams to define trustworthy KPIs, improve data quality, translate complex data into actionable insights, and prioritize analytics initiatives. The role combines people leadership with hands-on BI solution design and supports client-facing analytics offerings.

Supports data quality assurance projects by checking errors, identifying inconsistencies, profiling data, assisting with data management plans, and developing quality improvement initiatives. The intern conducts research, contributes to audit-compliance analytics, collaborates with team members, and learns PwC methodologies while gaining professional experience in data integrity and management.

Lead Data Engineer developing cloud-based data and AI solutions for risk management. Responsibilities include building, testing, deploying, and supporting full-stack systems; developing data pipelines, distributed and streaming applications; using cloud warehouses, RDBMS, NoSQL databases, and machine learning technologies; conducting code reviews and unit tests; collaborating across Agile teams; and mentoring engineers. The role requires application development, big data, and cloud experience, with preferred expertise in real-time data, data warehousing, distributed computing, and AI tooling.

Conduct quantitative and qualitative research, including data cleaning, statistical analysis, subgroup comparisons, regression, clustering, and significance testing. Manage online research communities for teens, parents, and counselors, overseeing recruitment, engagement, incentives, research programming, and member experience. Translate findings into dashboards, visualizations, reports, and presentations for nontechnical audiences. Collaborate cross-functionally to shape research agendas, deliver rapid insights, and advance data-driven initiatives.

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.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.

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.