Top Remote Analytics Engineer Jobs

Lead design and implementation of data models, write optimized SQL at scale, enable semantic layers and AI tooling, manage analytics infrastructure, build frontend data surfaces, and partner with product and engineering to structure source systems for reporting.

The Senior Analytics Engineer will build and maintain data foundations for corporate reporting, translating business requirements into data models, ensuring consistent metric definitions and investigating data discrepancies for reliable reporting.

Own the Finance & Operations data layer: ingest and model data from systems (NetSuite, Ramp, Pigment) into Snowflake/Omni, ensure data quality and metric governance, build dashboards and reports, collaborate with Finance/Ops stakeholders, and automate reconciliation and reporting workflows.

Design, build, and maintain governed analytical data models and curated datasets (Bronze/Silver/Gold) using SQL, dbt, and Snowflake. Partner with business stakeholders to translate requirements, validate data quality, perform reconciliations and testing, and prepare trusted datasets for BI tools to support reporting, analytics, and executive decision-making.

The Senior Power Systems Engineer will lead a team focusing on power systems modeling and analytics, mentor engineers, manage projects, and create various transmission and distribution system models and analyses.

Customer-facing role that architects and implements satellite and terrestrial connectivity solutions, conducts site surveys and POCs, oversees installations and testing, troubleshoots complex issues, builds dashboards and analytics pipelines, documents solutions, trains customers, and coordinates cross-functional teams for handover to service delivery.

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.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Implement, QA, and document tag management and data-layer specs across GTM, GA4, server-side pipelines, Shopify, and native apps. Collaborate with product and engineering to ensure tracking, pixel integrity, attribution, and experimentation event fidelity; support monthly audits, consent enforcement, and maintain centralized solution design documentation.

Build and maintain electricity cost modeling REST APIs and DBT data pipelines, translate SME requirements into designs, participate in technical reviews, improve engineering processes, and collaborate across remote squads to deliver energy analytics and battery performance simulations.

Build and maintain electricity cost modeling REST APIs and DBT-driven data pipelines. Translate SME requirements into engineering designs, participate in design reviews, improve engineering practices, and deliver scalable analytics and battery performance simulations to inform DER ROI.

Design, build, and operationalize scalable analytics engineering solutions on Databricks and modern cloud platforms. Implement metrics-as-code and semantic layers, create automated pipelines, enable product measurement and experimentation, define cohorts and KPIs, improve data quality and governance, and lead engineering best practices and stakeholder delivery for data products.

Own end-to-end analytics modeling for a business domain: build and maintain dbt models and business marts on Snowflake, develop the BI semantic layer, define consistent metrics, create self-serve data products, and partner with Finance, Sales, Marketing, CS, and RevOps. Validate upstream data with the Data Platform team and translate ambiguous business questions into production-ready data models.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Lead hands-on design, development, review, and optimization of production data pipelines and platform capabilities. Provide technical leadership, mentor engineers, drive CI/CD, observability, automation, and scalable data platform architecture to support analytics and ML workloads within an Agile environment.

Hands-on principal data engineer defining enterprise-scale data architecture, building and optimizing data pipelines/integrations for analytics and ML, deploying production code, mentoring engineers, and partnering with product and ML teams to transform and analyze data for actionable insights.

The AI Developer is responsible for designing and deploying AI solutions, including autonomous agents and predictive models, and integrating them into business operations.

Build and ship AI-powered features for GTM analytics: Genie agents, LLM-driven workflows, and in-app intelligence. Develop agentic workflows (prompts, retrieval, tool integrations), write Python/SQL against Databricks lakehouse and Lakebase (Postgres), evaluate AI outputs, and collaborate with engineers and stakeholders to deliver tested, production-ready code.

Lead an analytics implementation for Valvoline's Global Products segment, driving metrics standardization, reporting consistency, and data accessibility. Create a scalable data framework, improve decision-making through visual analytics, and coach internal teams on analytics best practices.

The role involves leading analytics implementation for Valvoline's Global Products, focusing on standardizing metrics, enhancing reporting, and building scalable analytics infrastructures using SAP Datawarehouse Cloud.

The role involves leading the implementation of an analytics solution for Valvoline's Global Products business segment, focusing on standardization and visualization of metrics, data access, and analytics capabilities.

The role involves leading analytics solutions for Valvoline's Global Products segment, implementing a comprehensive data framework, and enabling insights through effective visualization and analytics practices.

Lead the development of analytics infrastructure for clinical AI/ML products, focusing on product performance monitoring, data management, and cross-functional collaboration.

As a Senior Full Stack Software Engineer, you will own product features, design scalable systems, mentor teammates, and ensure coding excellence while collaborating with cross-functional teams.

Lead data science and analytics initiatives for healthcare, overseeing model development, data integrity, and large-scale automated analyses. Provide actionable insights, mentor teams, collaborate with cross-functional partners, evaluate payment integrity campaigns, and present findings to stakeholders to optimize revenue, quality, consumer experience, and healthcare costs.