Top Analytics Engineer Jobs in Boston, MA

Build and maintain production forecasting models for recognized revenue and COGS, connecting product usage, pricing, infrastructure costs, and unit economics. Partner with Finance, Product, Engineering, Data Science, and Go-to-Market teams on planning, scenario analysis, variance investigations, and model governance. Establish forecasting standards, monitor accuracy and drift, develop scalable data foundations, and communicate insights to technical stakeholders and executives.

Own Eve’s go-to-market data models and semantic layer for governed metrics including pipeline, conversion, CAC, win rate, ARR, and bookings. Establish certification frameworks, modeling standards, documentation, alerting, and analytical application patterns across the organization. Partner with Sales, Marketing, Customer Success, and RevOps stakeholders, resolve conflicting metric definitions, enable AI-grounded data tools, review contributions, and mentor analytics engineers.

Lead design, build, and deployment of AI-enabled analytics using Microsoft AI technologies and cloud data platforms. Develop AI assistants, copilots, RAG and agentic workflows, integrate solutions with Power BI and semantic models, prototype and productionize with Python and SQL, implement governance and observability, and partner with BI, data engineering, and business stakeholders to scale enterprise AI use cases.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, translates business requirements into data solutions, and contributes to data governance, security, and quality assurance. Collaborates with cross-functional teams supporting marketing, engineering, product development, supply chain, finance, business analytics, and AI initiatives.

Own Hometap’s product and marketing event-data ecosystem, including taxonomy, tracking plans, governance, Segment Protocols, schema validation, and source management. Partner with marketing, product, and engineering to define instrumentation and event specifications. Build monitoring for schema conformance, event volume, and property completeness, while documenting standards and managing event review and deprecation processes.

Build and maintain SQL and dbt data models, tables, views, dictionaries, and self-service tools on BigQuery. Support Accounting, Actuarial, and Claims stakeholders from request through production, monitor data quality, prevent defects, document processes, and review AI-generated code. The role requires hands-on data development, collaboration across projects, and growing ownership of a data domain and stakeholder relationships.

Build and maintain dbt-based data transformations, analytics models, ELT workflows, testing, and data quality processes. Collaborate with analysts, engineers, and stakeholders to support reporting, self-service exploration, governance, and business decision-making. Troubleshoot data issues, document workflows, evaluate analytics tools, and contribute to scalable data practices using a modern cloud data stack.

Own Step’s analytics data models, transformation pipelines, metric definitions, semantic layer, and data quality practices. Partner with Product, Marketing, Engineering, Risk, Growth, and Finance to establish trustworthy data foundations and enable self-service analysis. Build reusable query, visualization, and code components; apply AI-augmented workflows; and collaborate with data scientists, analysts, and platform engineers. The role requires strong SQL, modeling, pipeline, BI, communication, and independent ownership skills, with occasional travel for team onsites.

Provides technical leadership for enterprise analytics by architecting SQL-based transformations, scalable data models, semantic layers, and governed datasets. Builds analytics products supporting healthcare KPIs, patient journeys, scheduling, retention, and operational decisions. Establishes analytics engineering standards, testing, documentation, lineage, and code-review practices; troubleshoots data-quality issues; partners with business and technical stakeholders; and mentors analysts in SQL, dimensional modeling, dbt, and governed data-product development.

Owns the enterprise Snowflake data platform end to end, including architecture, ingestion, transformation, security, governance, reliability, performance, cost optimization, and AI enablement. Establishes engineering standards, reviews architecture, resolves complex production issues, mentors data professionals, and leads cross-functional adoption of scalable, governed analytics capabilities. The role provides senior technical influence without direct people management and supports secure MCP-based AI integrations.

Builds business-critical dashboards, scorecards, reports, and ad hoc analyses using SQL, Python, Excel, Power BI, and Tableau. Analyzes KPIs, identifies trends and root causes, automates recurring reporting, validates data, and translates complex findings into actionable insights for stakeholders and leadership. The role collaborates with data engineers and cross-functional teams to improve reporting workflows, data pipelines, and decision-making.

Develop and deploy Adobe Experience Cloud solutions, primarily Adobe Launch, Analytics, and Target, for enterprise clients. Responsibilities include requirements analysis, technical specification development, JavaScript and media-tag implementation, QA/UAT testing, solution design, effort estimation, client collaboration, mentoring, and supporting business development. The role requires managing multiple projects while delivering high-quality implementations within Agile development processes.

Build and optimize Customer 360 data models, ELT pipelines, semantic layers, and identity resolution frameworks in BigQuery. Develop Tableau dashboards for customer acquisition, retention, churn, product adoption, and deposits. Sync behavioral data with marketing and CRM platforms such as Braze, implement data quality and observability controls, and partner with Marketing, Product, and business leaders to turn customer analytics into growth initiatives.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake. Architect data foundations to power AI and LLM-enabled workflows, operationalize core SaaS metrics, and build reusable data assets for analytics and activation. Partner cross-functionally to embed analytics into workflows and ensure scalable, secure architecture.

Build and optimize Hipp’s analytics data platform, including production data pipelines, warehouses, schemas, dashboards, reports, and data models. Write complex SQL, monitor data quality, perform product and operational analysis, and translate business needs into technical solutions. Collaborate with product managers and data scientists to support AI and ML initiatives, deliver reliable insights, and inform product strategy.

Build scalable cloud-native data and machine learning solutions using Databricks, Spark, PySpark, and SQL. Develop batch and real-time healthcare data pipelines, enterprise data models, Lakehouse architectures, semantic layers, and AI-assisted analytics workflows. Partner directly with business and clinical stakeholders to discover requirements, prototype solutions, deliver demos, drive adoption, and provide technical leadership. Ensure HIPAA-compliant security, governance, lineage, auditability, and regulatory readiness.

Own executive, board, investor, financial, and operational reporting for leadership. Build scalable dashboards, define consistent KPIs, reconcile data across systems, investigate performance changes, and translate complex business questions into actionable insights. Partner with Strategy, Finance, and senior leaders to automate recurring reporting, improve data accuracy, and support actuals-versus-budget-versus-forecast analysis.

Build and maintain the full analytics data lifecycle, including Fivetran ingestion, dbt modeling, Snowflake data warehousing, semantic layers, ThoughtSpot visualizations, governance, testing, and observability. Partner with business teams to create trusted metrics and self-service data products. Maintain conversational analytics regression tests, enforce data access controls, optimize performance and costs, and use AI agents for data engineering tasks under established review standards.

Build, test, deploy, and operate data pipelines and transformations for financial and economic datasets. Monitor production workflows, implement alerting, logging, automated testing, and data-quality controls, and perform root-cause analysis. Collaborate with engineering, DevOps, application, investment, and research teams to deliver documented solutions. Analyze investment data, support model research, and identify automation opportunities while developing expertise in private markets data.

Principal technical leader responsible for architecting and operating relational data marts, dbt data products, semantic layers, and shared data services. Builds scalable solutions in Databricks, Delta Lake, and Spark; owns data products through testing, deployment, documentation, monitoring, and support. Establishes governed metrics, data quality, lineage, observability, and access controls while partnering cross-functionally to resolve ambiguity. Mentors engineers, improves reusable frameworks, and supports BI, data science, and AI use cases.

Build and maintain scalable analytics data products using SQL, dbt, Snowflake, and dimensional modeling. Design trusted warehouse tables, semantic layers, metrics, dashboards, and self-service tools for BI, data science, machine learning, and AI use cases. Partner with stakeholders, optimize performance and costs, enforce data governance and privacy, improve data discovery and lineage, and mentor teammates through collaborative development and code reviews.

Owns sales and creator-commerce data end to end, building tested and documented dbt models, semantic-layer metrics, and Omni dashboards. Partners with Sales, RevOps, Product, and data engineering to model CRM, pipeline, funnel, commerce, and product-usage data. Establishes trusted metric definitions, improves self-service reporting, enables AI-assisted data querying, reviews code, documents decisions, and serves as the primary owner of Kit’s sales data domain.

As a Lead Data Scientist, you'll design experiments, build predictive models, and lead a team while shaping data-driven strategies to enhance AI deployments.

Own the architecture, technical direction, reliability, and evolution of a shared cloud data platform. Lead hands-on development of streaming and batch pipelines using infrastructure-as-code, build governed analytical data products, and establish data contracts, quality, security, lineage, and observability standards. Serve as the final technical escalation point, mentor engineers, lead design and code reviews, promote responsible AI-assisted coding, respond to incidents, and communicate technical tradeoffs across stakeholders.