Top Analytics Engineer Jobs in Boston, MA

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.

Build, refactor, and operate a production RWD delivery pipeline using dbt, PySpark, Snowflake, and AWS. Own delivery endpoint configs as code, enforce HIPAA de-identification, manage monthly deliveries, drive data quality metrics and investigations, support channel partners, and collaborate with platform engineering using CI/CD, Argo orchestration, and observability tools.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Own and govern centralized data assets; design scalable dbt/Snowflake models; model Stripe subscription and payments data; set SQL standards, testing, monitoring and documentation; lead complex BI projects in Hex; partner with product, engineering, operations and GTM to deliver durable analytics solutions.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

The Data Engineer will design and build data solutions, develop ELT jobs, ensure data quality, and collaborate with technical teams to align data strategies with business goals.

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.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

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.

Design, build, and maintain scalable ELT pipelines and analytical data models. Ensure data quality, validation, and documentation. Collaborate with analysts, data scientists, and engineers to deliver reliable datasets and tooling for reporting and decision-making. Monitor, troubleshoot, and enforce data governance and security; finance domain experience is a plus.

As a Billing & Analytics Software Engineer, you'll design and maintain the billing pipeline, integrate billing systems, and develop customer-facing analytics dashboards to support revenue recognition and operational observability.

Build and maintain governed, activation-ready marketing data products in Snowflake. Design canonical marketing data models, event-streaming, identity resolution, quality checks, and reverse-ETL activation for marketing, CRM, paid media and AI use-cases. Enable predictive scoring, attribution, and AI-ready context layers while partnering with cross-functional stakeholders.

Design, build, and maintain scalable ETL/ELT pipelines and analytics-ready data models using Python and SQL. Implement GitLab CI/CD, develop dashboards in Tableau/Power BI, perform data cleaning and analysis, support modeling and AI initiatives, document metadata, and mentor junior staff while collaborating with cross-functional teams to deliver actionable insights.

Develop and deliver healthcare analytics solutions using Ursa Studio, reusable data models, data marts, and measures. Partner with clients and cross-functional teams to scope engagements, provide consulting and training, maintain documentation and security standards, and contribute scalable analytics components to Ursa’s SaaS and AI-assisted platforms. The role requires strong healthcare data expertise, SQL proficiency, client-facing communication, and knowledge of healthcare regulations and standards.

Build and maintain reliable data warehouse models and pipelines to power GTM metrics, dashboards, and automation. Partner with Sales, CS, Product, and Engineering on instrumentation, CRM and usage modeling, data quality, observability, forecasting, and actionable data products for GTM teams.

Design and implement ad measurement metrics, perform deep-dive analyses, build reporting and automated pipelines, create visualizations, and provide actionable insights to improve advertiser measurement and platform health.

Partner with Content Finance stakeholders to build and own end-to-end data products and semantic models. Define core metrics, maintain documentation and tests, perform root-cause analysis, improve pipelines, and drive cross-functional alignment. Mentor teammates, champion data-as-product practices, and leverage GenAI tools to accelerate development.

Build, maintain, and validate production data pipelines and ETL infrastructure (BigQuery/GCS/dbt/Airflow alternatives). Ensure data observability, SLAs, and automated tests to deliver reliable datasets that support campaign decision-making and organizers. Collaborate with product and data teams across fast-paced, cycle-driven work.

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.

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.

Build and maintain data pipelines, metrics, and dashboards to quantify consumer product security risk. Perform hands-on analytics into device and authentication signals, shape risk measurement strategy, partner with product and security teams, and influence leadership reporting. Drive a unified, scalable data foundation that enables cross-team alignment and proactive risk detection.

Bridge enterprise customers' legacy BI to Hightouch by reverse-engineering complex marketing reports, building reusable reporting frameworks, engineering and documenting semantic/context layers, ensuring accurate SQL generation, and training non-technical marketers while collaborating cross-functionally to deliver reliable, idempotent AI-driven analytics.

Build and maintain auditable data pipelines in Snowflake and dbt to ensure financial reconciliation and audit readiness. Partner with Accounting, Operations, Engineering, and Reporting to investigate data issues, improve integrations via APIs, and own claim reimbursement and contract sales data workflows.

Build and scale the analytics foundation: own dbt projects and Snowflake warehouse, create trusted datasets and semantic layers for AI, build ELT pipelines from APIs, implement testing/observability and CI/CD, design reconciliation models, enable stakeholder self-service, and partner cross-functionally to drive data-driven decisions across GTM, Product, Finance, People, and Operations.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.