Top Analytics Engineer Jobs in Boston, MA

Architect scalable data models and ELT pipelines using modern data lake technologies. Build self-service analytics products, establish enterprise data governance and observability standards, and lead data architecture across engineering teams. Partner with data scientists, product engineers, and business stakeholders to improve data literacy, identify root causes, and connect insights to business outcomes. Develop AI-enabled data tools and frameworks while providing technical leadership, mentorship, and direction for analytics engineering practices.

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.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Own end-to-end data pipelines and production-grade dbt models (Snowflake) for Sales and Product analytics. Build governed data marts, dimensional models, and reverse ETL workflows. Improve data quality, testing, monitoring, and documentation. Partner with GTM, Product, and BI stakeholders, mentor peers, and contribute to platform tooling (Airflow, Terraform, AWS) to enable reliable, scalable analytics.

Lead the architecture and migration of Rula’s data platform to Snowflake and Medallion architectures. Govern warehouse performance and cloud costs, establish data contracts and intake guardrails, and enforce GitHub-based CI/CD testing for data quality and query performance. Scale AI-assisted engineering practices across teams while partnering with executives and domain engineers. The role also supports secure clinical data handling, schema evolution, semantic BI integrations, and production ELT pipeline development.

Build and own end-to-end data products, including pipelines, transformations, semantic models, dashboards, metrics, and AI-enabled workflows. Partner with business and technical stakeholders to turn ambiguous questions into trusted, maintainable solutions. Establish data quality testing, documentation, monitoring, and governance while improving architecture and automation. Use Snowflake, dbt, Power BI, and related technologies to support analytics across Marketing, Product, Finance, and Engineering.

Design and maintain analytical data models, transformation frameworks, data-quality checks, semantic definitions, and metric consistency across analytics and customer-facing products. Partner with engineering, product, and analytics teams to document data, improve lineage, and enable trustworthy self-service data usage. The role requires advanced SQL, analytics engineering, dimensional modeling, testing, and collaborative translation of business concepts into precise data definitions.

Own product analytics and customer success data domains by building governed models for product usage, feature adoption, customer health, onboarding, renewals, churn, expansion, and net revenue retention. Partner with Product, Customer Success, RevOps, Finance, and analysts to create semantic models, metric definitions, instrumentation, documentation, alerting, and self-service tools. Use dbt, Snowflake, Omni, Hex, and AI-assisted development to turn ambiguous stakeholder questions into durable data products.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Architect and maintain NetBrain’s first cloud data warehouse, ELT pipelines, transformation layer, semantic models, and BI foundation. Own data quality, governance, lineage, access controls, and curated models supporting product usage, customer adoption, account health, and go-to-market reporting. Partner with stakeholders to establish reliable metrics and a standardized source of truth, while supporting AI-assisted analytics and natural-language reporting use cases.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting identity graph construction, entity matching, and identity resolution. Partner with engineering and data science teams to productionize matching logic, evaluate match quality, monitor data quality and drift, and manage identity-provider relationships. Translate complex identity concepts into actionable insights for technical and non-technical stakeholders while helping define privacy-conscious product and data priorities.

Own and architect marketing and storefront analytics systems, including metric definitions, single sources of truth, multi-touch attribution, marketing mix modeling foundations, semantic models, and data health metrics. Partner with marketing, storefront, and leadership teams to improve acquisition measurement, budget allocation, data quality, and decision-making across international markets.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Develops healthcare analytics reports, dashboards, datasets, and datamarts; analyzes program data to identify trends and outcomes; communicates findings to stakeholders; maintains self-service solutions; follows data stewardship and sensitive-data handling standards while balancing multiple projects.

Develops business intelligence, reporting, data pipelines, dashboards, and geospatial solutions for complex real estate portfolios. Transforms and integrates data from multiple sources, builds scalable analytics products, automates reporting processes, and supports portfolio planning, occupancy strategy, workplace initiatives, and operational decision-making. Partners with stakeholders and clients to translate business needs into technical solutions, communicate insights, train users, and improve analytics adoption. The role also supports geospatial capabilities and leverages emerging AI-enabled tools.

Implements and supports security data ingestion pipelines across applications, cloud services, infrastructure, security tools, and business systems. Develops transformations, parsing, normalization, enrichment, masking, validation, and delivery workflows. Troubleshoots data quality and pipeline issues, documents architectures and data flows, coordinates with client stakeholders, and supports SIEM, detection, investigation, observability, and automation use cases. Requires data engineering experience, scripting, query languages, integration knowledge, and strong client communication.

Build and maintain dbt analytics models using SQL and Jinja, implement data quality tests and freshness checks, create Metabase dashboards, perform ad hoc analysis, and collaborate with finance, product, sales, and customer success stakeholders. The role requires advanced SQL, data modeling, analytics engineering, documentation, statistical analysis, Git-based development, and CI/CD experience, with Snowflake, Python, and data analysis tools preferred.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

Own and improve Addgene’s BigQuery data warehouse, including data modeling, documentation, quality validation, and pipeline development. Build reliable datasets, establish data definitions and engineering standards, integrate internal data sources, automate repetitive work, and support dashboards and reporting. The role requires strong SQL and Python skills, dbt experience, proactive problem-solving, code review participation, and the ability to work independently in a cross-functional environment.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Lead the architecture and technical standards for Curaleaf’s data and analytics ecosystem. Design secure, scalable Snowflake environments; establish SQL, Python, dbt, modeling, testing, CI/CD, and governance practices; and develop reusable data products and platform capabilities. Partner with engineering, analytics, data science, governance, and business teams, guide technical roadmaps, optimize performance and costs, lead incident reviews, and mentor engineers through cross-team technical influence without direct people management.

Build and scale an enterprise metrics store, dbt models, semantic-layer definitions, and analytical workflows. Develop governed business metrics, root-cause analysis, metric relationships, and reusable logic for BI tools. Partner with product, engineering, sales, underwriting, claims, and experience teams to translate business questions into actionable insights. Improve data quality, define data contracts, debug upstream and analytical issues, and help prepare metric infrastructure for API-based serving.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.