Top Analytics Engineer Jobs in Boston, MA

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.

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.

Leads data and analytics engineering across cross-functional teams, translating business needs into technical solutions, managing data models and context layers, and ensuring data quality, governance, and accessibility. Establishes innovation environments, develops training and cross-skilling programs, mentors data professionals, supports self-service analytics and AI capabilities, and drives continuous improvement, technical reviews, and strategic data initiatives.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Build and maintain HR data pipelines and dbt models on GCP and BigQuery, integrating systems such as Workday and Greenhouse. Develop self-service dashboards, recruiting reports, data validation, governance, and AI-powered analytics experiences. Translate HR and recruiting data into actionable insights, improve reporting automation, and support stakeholders with decision-ready information while helping establish scalable analytics processes.

Build and oversee reliable ETL/ELT pipelines and data products that transform complex customer data into trusted analyses, visualizations, quality reports, and AI-powered insights. Responsibilities include data exploration, quality control, anomaly detection, AI/ML output evaluation, customer collaboration, documentation, and improving data-cleaning and analytics tools. The role requires cross-functional work in an Agile environment focused on security, data integrity, and clear communication.

Build and maintain marketing data pipelines connecting Salesforce, Marketo, and cloud data warehouses. Automate campaign reporting, develop SQL datasets and views, improve attribution and data quality, support CRM and marketing automation configuration, and document data models and processes. Use AI tools such as Claude to accelerate analytics workflows while ensuring reporting accuracy. Partner with Marketing Analytics, Demand Generation, and Marketing Operations to support executive-level pipeline reporting.

Contract Analytics Engineer maintaining Looker and LookML dashboards, reporting, data operations, sales pacing, data QA, vendor workflows, board reporting, forecasting, and planning support during a director’s maternity leave. The role requires strong SQL and Snowflake skills, analytics engineering experience, vendor management, Git/GitHub proficiency, documentation, and independent work in a lean data team.

Designs and maintains credit loss models, analytical solutions, data workflows, automated pipelines, reports, and dashboards supporting CECL, stress testing, underwriting, reserving, forecasting, and risk management. Partners with Finance, Risk, IT, Data Management, and business teams to translate requirements into scalable solutions. Documents model development, testing, governance, and regulatory compliance activities while addressing data quality and reporting challenges.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Build and maintain scalable ELT pipelines, dbt models, semantic layers, and governed product analytics assets. Partner with Data Engineering, Product Engineering, Data Governance, analysts, and product managers to define trusted metrics, monitor data quality, document taxonomies, and support self-service analytics. Translate product usage telemetry into certified metrics for expansion and upsell analysis, while communicating technical concepts to technical and non-technical stakeholders and driving engineering best practices.

Lead end-to-end analytics for Netflix Ads demand, including media planning and campaign management tools, machine learning models, and agentic solutions. Partner with scientists, product managers, engineers, and data engineering to develop metrics, reliable analytics data, insights, and recommendations. Independently identify risks and opportunities, shape product roadmaps, and communicate findings to technical and non-technical stakeholders.

Build and expand the company’s analytics data stack using Looker, LookML, dbt, BigQuery, and Airbyte. Develop data models, integrate multiple business systems, create KPI dashboards, and analyze product usage, customer health, adoption, and revenue data. The role empowers business teams with self-service analytics, actionable insights, and data-informed decision-making while developing a strong understanding of business metrics.

Own the customer experience data foundation by designing dbt models, pipelines, data quality tests, documentation, semantic layers, dashboards, and self-service datasets. Integrate support, KYC, withdrawal, CRM, workforce management, and market resolution data into the cloud warehouse. Identify recurring support issues, communicate findings to stakeholders, and partner with data engineering on infrastructure and governance.

Designs and governs scalable dbt data architecture, analytics models, testing, documentation, metadata, and semantic-layer standards. Builds agent-safe infrastructure and self-service analytics experiences in Hex, improves data quality and reliability, and partners with engineering, data science, and business teams. The role also influences roadmap decisions, mentors teammates, and develops reusable tooling and frameworks for trusted, cost-efficient cloud data products.

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.

Build and steward Netflix advertising quality-of-experience data, from device telemetry and instrumentation through data pipelines and dashboards. Collaborate with product engineering, data engineering, and data science teams to measure user-perceived performance, improve data quality, document user flows, and establish monitoring and measurement best practices. The role requires strong SQL and Python skills, technical communication, stakeholder management, and comfort working independently in an ambiguous environment.

Build and own analytical data products, dbt models, semantic layers, shared metrics, and data quality systems. Model GA4 and digital analytics data, activate trusted data through reverse ETL, and partner with analysts, product teams, and business stakeholders to support decisions. Mentor engineers and analysts, establish modeling standards, and contribute to self-service and AI-assisted analytics foundations.

Design and evolve distributed search and analytics systems for the Splunk platform. Lead complex engineering efforts from design through delivery, improve performance and reliability, mentor engineers, collaborate cross-functionally, and work with customers to resolve production issues.

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 and maintain scalable dbt-based data models and analytics-ready datasets for pharmacy benefit management and care navigation. Translate healthcare workflows into auditable data products, implement data quality testing and reconciliation, optimize warehouse performance, and establish documentation, lineage, governance, CI/CD, and observability practices. Collaborate with technical and business teams to support reporting, financial analysis, self-service BI, and future AI-enabled analytics capabilities.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Lead frontend engineering for the DataLab product, developing scalable React and TypeScript interfaces for enterprise financial applications. Guide frontend architecture, component design, accessibility, performance, developer experience, and testing practices. Collaborate with product managers and backend engineers, mentor teammates, make technical decisions, and promote AI-assisted coding tools while maintaining high standards for security, quality, and maintainability.

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.