Top Data & Analytics Jobs in Boston, MA

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.

Build and maintain dbt models, Snowflake semantic layers, ingestion pipelines, and monitoring across finance, operations, IoT, and marketing. Resolve complex data issues, improve data quality and resilience, translate ambiguous requests into delivery plans, and contribute to AI-powered data products. The role also involves integrating systems such as Salesforce, NetSuite, Zendesk, and ADP while documenting and strengthening data engineering standards.

Design and execute complex analyses evaluating property claims operations, process changes, tools, and vendor partnerships. Build reporting frameworks, synthesize multi-source data, and translate findings into actionable recommendations for senior stakeholders. Independently lead analytical projects that improve financial outcomes, claims handling, customer results, and operational efficiency.

Participate in a rotational analyst development program focused on using data and technology to analyze performance trends, identify business opportunities, and support insights within the insurance industry. Complete curated training, mentorship, and rotational assignments while developing analytical, technical, strategic, communication, and business skills. Use tools such as Excel, SQL, Python, R, SAS, Snowflake, Power BI, and emerging AI technologies.

Leads the Property Claims Loss Insights analytics team, setting analytical strategy, developing talent, guiding complex analyses, and translating findings into recommendations for executive and business audiences. The role evaluates financial and operational impacts of claims tools, vendors, and process changes; prioritizes high-value work; ensures methodological rigor; challenges assumptions; and partners with leadership to improve claims handling and financial outcomes.

Supports PwC Deals diligence analytics projects through data gathering, analysis, research, visualization, data mining, algorithm development, and preparation of reports and presentations. The intern will use Alteryx and Power BI, integrate data from multiple sources, develop data-driven insights for clients, and learn from advisory professionals while contributing to cloud, data architecture, and AI-related initiatives.

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.

Own and improve e-commerce fulfillment analytics capabilities, including solution design, coding, deployment, data pipelines, validation, modeling, reporting, and visualization. Analyze internal and external data to generate insights for network strategy, capacity, investment, sales capture, and cost management. Partner with data, fulfillment, brand, and executive teams while managing roadmaps, vendors, documentation, testing, and continuous improvement.

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.

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.