Top Data Analyst Jobs in Boston, MA

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.

Build and maintain the company-wide data foundation for FinOps; develop pipelines, dashboards, and operating scorecards; support stakeholders across commercial, finance, clinical ops, and operations; validate metrics with analytics engineers; transform messy datasets into actionable insights and adopt AI-powered workflows to improve efficiency.

The Data Architect/Data Analyst will transform business requirements into technical specifications, evolve data models, and communicate designs to stakeholders. Responsibilities include collaborating with ETL developers, maintaining documentation, and optimizing database architectures.

Build governed datasets and applied AI solutions for supply chain automation and pre-sales engineering. Develop forecasting, scenario modeling, optimization, benchmarking, and document intelligence workflows using Databricks, SQL, and Python. Publish reliable data products to Tableau Cloud, manage pursuit artifacts, and establish closed-loop learning by comparing design assumptions with operational outcomes. Partner with engineering, analytics, and solution design teams to improve automation proposals, estimates, and customer outcomes.

Supports a global Microsoft Dynamics 365 ERP integration strategy by configuring Finance & Operations and Customer Engagement modules, establishing legal entities, defining business requirements, and resolving system and data issues. Leads functional testing, UAT, deployment, test automation, quality assurance, and data migration validation. Collaborates with legal, finance, operations, and technical teams to improve business processes, automate workflows, close process gaps, and ensure accurate, effective ERP implementations.

Leads quality control and review of preclinical imaging data across modalities, ensuring accuracy, consistency, completeness, and compliance with project requirements. Supports QC team leadership, resolves complex data integrity issues, mentors analysts, reviews study reporting, identifies process bottlenecks, and collaborates with project management, image analysis, laboratory, study director, analysis, and software teams to improve workflows and develop SOPs.

Analyzes complex business processes, data requirements, and information systems supporting the Defense Logistics Agency. Applies DoD Business Enterprise Architecture, data modeling, enterprise architecture, systems integration, and process analysis expertise to improve data quality, interoperability, modernization, and enterprise decision-making. This fully remote role requires Secret clearance and supports AMPS program initiatives.

Loads, queries, extracts, validates, and refreshes client data; prepares reports and deliverables; identifies automation and process-improvement opportunities; supports UAT, system implementations, and quality assurance testing; and collaborates with cross-functional teams. The role translates client requirements into actionable tasks and requires SQL, relational database, Oracle, Unix, data analysis, reporting, and communication skills.

Turn complex data into actionable insights for National Accounts by performing statistical analysis, forecasting, modeling, and automated reporting. Build dashboards and executive presentations, diagnose data quality, partner with account teams, data engineers, and finance to influence decisions and track performance metrics.

The role involves operating and improving AI-driven data quality pipelines, managing offshore teams, and ensuring data accuracy and reliability. Responsibilities include monitoring pipeline performance, refining rules, and collaborating across teams to enhance data operations.

Perform independent healthcare analytics projects for clients: use statistical tools, predictive modeling, BI/reporting and data visualization. Build reports and insights using R, SAS, SPSS, Cognos, and Business Objects, and collaborate with the Senior Consultant to maintain client relationships.

Lead client-facing analytics engagements: translate ambiguous business questions into data models, dashboards, and KPIs; write production-quality SQL and dbt models; build BI reporting; apply AI tools to accelerate analysis; drive proactive insights; set stakeholder cadence; and mentor junior analysts to ensure adoption and delivery.

Analyze large datasets using SQL and Python, build dashboards and recurring reports, identify trends and data-quality issues, and translate ambiguous business questions into actionable insights. The role partners with product, customer success, and engineering teams, documents metrics and methodologies, and presents findings to technical and non-technical stakeholders. Healthcare data, supply chain analytics, modern analytics engineering, and BI visualization experience are advantageous.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.

Supports enterprise data programs involving master and reference data, data governance, stewardship, lifecycle management, quality, reporting, analytics, and modernization. Analyzes data, processes, requirements, and stakeholder needs; documents business requirements and data definitions; partners with business, technology, and data teams; and contributes to reporting, process improvements, platform enhancements, and cross-functional initiatives. This is an 18-month early-career program for students graduating in May 2027.

Leads development and delivery of complex healthcare, clinical, provider, and financial datasets. Provides enterprise reporting, dashboards, data visualization, business intelligence, and decision support. Analyzes cost, utilization, provider, member, and clinical data to identify inefficiencies and savings opportunities. Serves as an internal analytics consultant, improves data procedures, automates reporting processes, trains team members, and ensures validation and documentation standards.

Analyze marketing and media data to measure TV and streaming campaign effectiveness, validate data and assumptions, identify actionable insights, and create clear visualizations and client-ready presentations. The analyst will use Excel, BI and data platforms, SQL, and AI tools to accelerate analysis while independently validating results and explaining methodologies, KPIs, and recommendations to internal teams and clients.

The Senior Data Analyst gathers BI requirements, analyzes enterprise and offline datasets, writes and optimizes SQL queries, and builds dashboards using Power BI, Tableau, and Domo. The role translates business needs into technical specifications, designs dashboard wireframes, validates data, documents processes, and collaborates with engineering, product, and business teams. Knowledge of the US trust business is essential for interpreting data within financial and regulatory contexts.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Leads Gartner research and thought leadership on data management, governance, and AI. Develops actionable insights, analyzes market trends, advises executive clients, delivers presentations, supports sales and conferences, reviews content, improves research processes, and mentors analysts. Requires deep expertise in data and analytics, AI implementation, data security, data value creation, executive communication, research, and strategic analysis. Remote U.S. role with travel up to 25%.

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.

Own data quality across systems by designing validation rules, reconciliation processes, profiling workflows, and quality controls. Investigate discrepancies to identify root causes, apply statistical methods to detect anomalies, evaluate machine learning model outputs, and communicate findings to technical and non-technical stakeholders. Partner with engineering, BI, and business teams to establish data quality standards, metrics, controls, and reporting practices.

Analyze marketing data to uncover acquisition, retention, funnel, and campaign insights. Build Tableau and Looker Studio dashboards, evaluate ad spend and marketing performance, support A/B testing, integrate and validate data from platforms such as Google Ads and GA4, and provide recommendations to marketing, product, and customer experience teams. The role also improves reporting workflows and translates complex analytical findings into actionable business guidance.

The Data Analyst position requires U.S. citizenship and that all work be performed within the continental United States, Alaska, or Hawaii. The listed annual pay range is $90,000 to $110,000.

Analyzes, validates, and maintains provider data for credentialing, enrollment, health plan rosters, and regulatory submissions. The role performs data reconciliation, quality checks, reporting, exception resolution, and audit support while collaborating with credentialing, operations, compliance, contracting, enrollment, and IT teams. It also supports process improvement, automation, data governance, system testing, and documentation to improve provider data accuracy and submission timeliness.