Top Data & Analytics Jobs in Boston, MA

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.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Owns and evolves governance standards for a data marketplace, including asset eligibility, certification tiers, ownership models, documentation, access requirements, and compliance reviews. Partners with domain, security, and product teams to resolve governance exceptions, address recurring data issues, and inform marketplace strategy, roadmap, and success metrics.

Analyzes financial risk data and develops data transformation, reporting, dashboard, and visualization solutions for banking and capital markets clients. Supports quantitative analytics involving capital, liquidity, credit risk, and asset-liability management. The role also includes client collaboration, quality review, mentoring junior staff, process improvement, and automation of data movement and reporting.

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.

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.

Designs reusable data ingestion and transformation components, automates data quality, governance, testing, lineage, and deployment workflows, and improves pipeline observability and reliability. The role supports monitoring, incident response, cloud and container integrations, infrastructure as code, documentation, reusable datasets, and Agile platform improvements. Candidates need five or more years of data engineering experience plus expertise in orchestration, distributed processing, storage, scripting, automation, and version control.

Leads the Journey Intelligence Team’s analytics strategy, develops analysts, and builds measurement frameworks, segmentation strategies, and scalable data assets. Conducts exploratory analysis, experimentation, and customer lifecycle research to identify engagement opportunities. Partners with Product, Operations, and senior leadership to translate customer behavior insights into strategic initiatives and measurable improvements across the customer journey.