Top Data & Analytics Jobs in Boston, MA

Analyzes operational, customer survey, qualitative, and unconventional data using Snowflake and SAS to identify retention drivers and customer pain points. Synthesizes findings into recommendations, improves reporting, provides operational consulting, and presents insights to senior leadership. Requires strong statistical, analytical, communication, and cross-functional collaboration skills.

Supports Wealth Management and the MMLIS Advisor Loan Program through financial analysis, deal and loan modeling, profitability and sensitivity analysis, dashboards, data visualization, and business insights. Uses SQL, Python, Excel, and BI tools to analyze complex datasets, automate workflows, identify trends, and develop KPIs. Partners with business and technology teams to improve analytics processes, support AI and automation initiatives, and communicate recommendations to leaders.

Develop advanced statistical models and custom analyses to solve client business challenges. Use R, Tableau, SAS, Visual Basic, VBA, Excel, and related technologies to analyze data, guide decisions, and create client deliverables. Present findings, manage client relationships, provide project updates, implement solutions, and mentor Associates. The role requires strong quantitative education, programming or analytics experience, communication skills, teamwork, and availability for full-time work in 2026.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Analyze how customers use Hi Marley to guide product, customer success, and company decisions. Define feature success metrics, evaluate adoption funnels, measure pilots and utilization, present customer-facing insights, and establish trusted self-service reporting. Partner with Data Engineering to translate analyses into governed data models and communicate findings clearly to executives and customers.

Leads the Sales Analytics team supporting Sales and Customer Success across three markets. Oversees business intelligence tools, dashboards, sales data modeling, retention and revenue analysis, and go-to-market measurement. Modernizes data workflows using Snowflake, dbt, Git, and tested models; develops Gong transcript use cases with LLM-based extraction; stewards Salesforce data; and partners with Sales, Revenue Operations, Engineering, and leadership to drive data-informed decisions.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Responsibilities include data modeling, architecture, distributed processing, relational and NoSQL databases, data security, observability, and real-time workloads. The role collaborates with product and engineering teams, establishes reusable engineering patterns, communicates technical concepts, and mentors data professionals while delivering resilient solutions.

Leads advanced analytics, reporting, scalable data models, dashboards, and data pipelines supporting Business and Marketing teams. Partners with senior stakeholders and AI Engineering to implement AI and machine learning workflows, predictive insights, and automation. Develops documentation, drives analytics engineering best practices, and mentors team members. Requires strong SQL, Python, APIs, data modeling, stakeholder communication, project leadership, and blockchain expertise.

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.