Top Data & Analytics Jobs in Boston, MA

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.

Own supervisory data management and governance as a subject matter expert. Define data quality rules, metrics, monitoring, issue remediation, taxonomies, metadata, and cataloging practices. Partner with business, IT, and platform teams to improve data reliability, accessibility, lifecycle management, and future AI readiness. Translate business needs into technical requirements, facilitate adoption of governance practices, and communicate recommendations to technical and executive audiences.

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.

Build and operate scalable data pipelines and models on Snowflake and AWS for Global Liquid Markets investment products. Responsibilities include integrating market and vendor data, implementing automated testing and governance standards, developing AI-powered engineering workflows, collaborating with product and business stakeholders, contributing to the data roadmap, mentoring engineers, and participating in code reviews. The role requires strong communication, architecture, agile delivery, data engineering, and responsible AI skills.

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.

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.

Develop market intelligence, forecasting, statistical models, scenario analyses, and automated reporting for supply chain decisions. Integrate internal and external datasets using Python, R, SQL, and other tools; build dashboards; support data governance; and translate analytical findings into recommendations for procurement, planning, and executive stakeholders.