Top Remote Analytics Engineer Jobs

The role involves gathering requirements for BI and data projects, providing documentation and support, and analyzing data to facilitate decision making and system improvements. The candidate will liaise between business units and IT, support application deployments, and drive transformation projects.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

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 and maintain production-grade data models and pipelines for the Compliance Data Mart, implement data quality frameworks and monitoring, partner with engineering to resolve data gaps, support regulatory exams and audits, and automate manual workflows into scalable pipelines and self-serve tooling.

The Staff Analytics Engineer will own the Financial Subledger Data Platform, build dbt models, implement data quality controls, and mentor a junior engineer, ensuring high operational reliability and cross-functional collaboration.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Design, build, and operate production-grade AI tools, automations, and integrations for HR workflows. Lead LLM-based solutions (prompting, RAG), integrate people systems (Workday, Slack, Google Suite), improve data quality, support end users, and drive measurable productivity and product roadmap decisions.

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.

Lead architecture, build, and operate customer-facing analytics platform: design ELT and metric governance, implement low-latency serving (OLAP), ensure reliability/performance, mentor engineers, partner with product, and use AI coding tools to accelerate development.

Designs, builds, and operates ELT data pipelines and analytical datasets for Platform Analytics. Curates and models source data into conformed datasets, ensures performance, reliability, and cost efficiency, contributes to target-state architecture, mentors junior engineers, and uses AI coding tools to accelerate development and delivery.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Design, build, and maintain scalable analytics data models and pipelines in AWS/Redshift. Collaborate with data engineering and business stakeholders to translate requirements, support BI dashboards, enforce schema and naming standards, maintain documentation and metrics, ensure data quality with tests and alerts, and promote best practices like version control and code reviews.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

Own and improve the dbt modeling and semantic layers, implement CI/CD, tests, observability, and governance; build governed metrics for BI and AI consumption; partner with Data Engineering and Risk to stabilize pipelines and improve data quality and documentation.

Own and build reliable analytics data products: define metrics, create and maintain dbt models, design dashboards for growth/finance/ops, improve data quality via testing and monitoring, and enable self-serve analytics for stakeholders.

The Analytics Engineer will design and implement analytics solutions, maintain reporting dashboards, partner with Product on KPIs, and support marketing with analytics.

As a Senior Analytics Engineer, you'll build data pipelines and dashboards, partner with stakeholders for KPI development, and ensure data quality across the company.

The GTM Analytics Engineer will enhance data usage across the revenue organization by refining models, designing workflows, automating processes, and partnering with teams to improve GTM strategies.

The role focuses on implementing cost optimization solutions for cloud infrastructure through automation and collaboration across teams, enhancing FinOps practices.

Owner of disbursement efficiency: analyze payment rails and routing KPIs, investigate transaction failures, build dashboards and dbt models, partner with Disbursements, Operations, and Product to improve success rates, cost, speed, and user experience.

Design, build and maintain scalable, auditable analytics-ready data models (primarily in dbt) for PBM and care navigation. Implement data quality checks, CI/CD, documentation, pipeline orchestration, and partner with stakeholders to enable self-service analytics and AI-enabled workflows.

Lead a team of developers on technology projects, collaborate on cloud-based solutions, and mentor peers while utilizing various programming languages and technologies.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

As an Analytics Engineer, transform raw data into metrics, manage data pipelines, ensure data integrity, and collaborate with teams for analytics solutions.