Top Remote Analytics Engineer Jobs

The Senior Data Analytics Engineer will develop data pipelines, support data-driven decision-making, collaborate with stakeholders, and improve data quality and processes.

The role involves managing analytics and data ecosystems, transforming data into insights, developing data layers and dashboards, analyzing datasets, and ensuring data privacy. Collaboration with cross-functional leaders is essential.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

The Analytics Engineer transforms raw data into reliable datasets, ensuring data quality for dashboards and decision-making at Civitech. They collaborate with teams to document models, write tests, and improve analytics practices.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

The Disbursement Analyst ensures efficient money transfer processes at Felix, analyzing data to optimize disbursement metrics and resolve routing issues while boosting user experience for Latino immigrants.

The Data Analyst will enhance remittance operations by analyzing transaction data, improving disbursement processes, and designing performance metrics dashboards while ensuring user satisfaction and operational efficiency.

The Disbursement Analyst will optimize transaction processes by analyzing data, maintaining performance dashboards, driving defect reduction, and communicating insights to stakeholders, focusing on improving user experiences in remittances.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Own and operate the Salesforce CRM Analytics practice: build and maintain CRMA dashboards, SAQL queries, dataflows and datasets; ensure performance, security, and data quality; integrate Data Cloud; collaborate with architects and analysts; mentor junior staff.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

Work with Product, Business, and Engineering to collect, clean, analyze data, build Python and SQL automation, contribute to internal analytics platforms, assist cloud deployments, and document technical solutions to support decision-making.

Develop, deploy, and maintain Oracle-based reporting solutions and dashboards (OAC, OTBI, FDI). Manage integrations and data migrations for Oracle Financials Cloud, perform system maintenance and performance tuning, build reporting strategy and documentation, partner with finance stakeholders to gather requirements, train users, ensure compliance and controls, and support analytics projects, upgrades, and issue resolution.

The Senior Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

Design, build, and maintain dbt-based analytics-ready data models and curated data marts powering a PBM financial risk engine. Onboard and optimize claims, pricing, contract, and utilization data, implement data quality controls, support forecasting and risk metrics, and enable advanced analytics and ML operationalization for finance and underwriting stakeholders.

The Senior BI Analytics Engineer will design and deliver data models for analytics, ensuring data quality, and collaborating across teams to enhance AI-driven reporting and querying capabilities.

Design and maintain scalable dbt data models and Looker dashboards; partner with Finance, Product, and GTM to translate requirements into analytics solutions; ensure data quality with Data Engineering; perform analyses, forecasting, and QA to deliver trusted reporting and drive analytics initiatives.

Lead full-stack analytics at Air by designing and maintaining dbt models and BigQuery transformation layer, ensuring data quality and observability, owning ingestion/orchestration, building self-serve BI (Mode/Hex) and AI-native interfaces, and partnering with product, growth, and GTM teams to deliver trusted data products and strategic insights.

The Enterprise Performance Analytics Engineer builds and maintains data pipelines and models, ensuring data quality and efficient reporting for analytics teams.

As an Analytics Engineer at Axios, you'll design and maintain data products and visualizations, improving decision-making through actionable insights.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Design, build, and maintain scalable, documented data models and the Gold data layer using dbt/Coalesce. Ensure data quality through automated tests, observability, version control, and CI/CD. Optimize SQL and warehouse performance, collaborate with Data Engineering and Analytics, and support semantic layers and BI catalogs to enable trusted, self-service insights for Product, Finance, and GTM teams.

Partner with business stakeholders to design and build data models and production-ready data products using Snowflake, dbt, and Power BI. Implement SQL-based transforms, optimize Power BI semantic models (DAX, Power Query), define metrics, create data quality tests, and collaborate with data engineers via Git-based workflows.