Top Remote Analytics Engineer Jobs

Own end-to-end analytics engineering for AI and developer-experience telemetry, including instrumentation, production pipelines, governed metrics, monitoring, executive dashboards, and analysis. Define and validate new measures of AI productivity and ROI, communicate findings to senior stakeholders, and recommend improvements to tooling, processes, and investment. Collaborate with data engineering, applied AI, and data science teams while using AI coding agents to build and maintain pipelines and reporting.

Own AI and developer-experience analytics end to end, including telemetry instrumentation, production data pipelines, metric governance, executive dashboards, and analysis. Define trustworthy metrics from evolving signals, monitor pipeline quality, communicate insights to senior stakeholders, and recommend actions to improve AI ROI and developer productivity. Use AI coding agents to build and maintain pipelines, dashboards, and analyses while partnering with data engineering, applied AI, and data science teams.

The Senior Analytics Engineer designs, develops, tests, and maintains data architecture, ELT pipelines, data models, data marts, metadata repositories, and data warehouses. The role optimizes data retrieval and quality, supports dashboards and self-service analytics, partners with stakeholders, guides technical initiatives, and mentors junior engineers. Responsibilities include delivering analytics engineering solutions end-to-end, maintaining data standards, building proof-of-concepts, and enabling actionable business insights.

Build and operate batch and near-real-time data pipelines, semantic models, certified measures, APIs, and serving infrastructure for AI-powered decision systems. Responsibilities include data quality, observability, lineage, governance, CI/CD, infrastructure as code, performance optimization, model scoring pipelines, and AI telemetry. Partner with data scientists and business stakeholders to productionize models and deliver reusable, documented data platform capabilities.

Builds and maintains reports, dashboards, data marts, and self-service analytics solutions for healthcare stakeholders. Validates dataset integrity, analyzes healthcare program outcomes, translates business needs into metrics and visualizations, and follows data stewardship and sensitive-data handling standards. The role partners with senior developers and data architects while developing expertise in healthcare systems and business processes.

Develops healthcare analytics reports, dashboards, datasets, and datamarts; analyzes program data to identify trends and outcomes; communicates findings to stakeholders; maintains self-service solutions; follows data stewardship and sensitive-data handling standards while balancing multiple projects.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Own end-to-end analytics pipelines for AI usage, developer experience, engineering productivity, and spend. Instrument telemetry, build governed ETL workflows, define trusted metrics, and create executive dashboards and visual narratives. Analyze results, recommend improvements to AI ROI and developer experience, and communicate methodology to senior stakeholders. Collaborate across data engineering, applied AI, and data science while using AI coding agents to build, test, and maintain production data systems.

Build and own end-to-end data products, including pipelines, transformations, semantic models, dashboards, metrics, and AI-enabled workflows. Partner with business and technical stakeholders to turn ambiguous questions into trusted, maintainable solutions. Establish data quality testing, documentation, monitoring, and governance while improving architecture and automation. Use Snowflake, dbt, Power BI, and related technologies to support analytics across Marketing, Product, Finance, and Engineering.

Design and maintain analytical data models, transformation frameworks, data-quality checks, semantic definitions, and metric consistency across analytics and customer-facing products. Partner with engineering, product, and analytics teams to document data, improve lineage, and enable trustworthy self-service data usage. The role requires advanced SQL, analytics engineering, dimensional modeling, testing, and collaborative translation of business concepts into precise data definitions.

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.

The Analytics Engineer develops end-to-end analytics solutions and business applications to streamline Operations and enable digital transformation. Responsibilities include communicating analytics to nontechnical stakeholders, supporting cross-functional supply chain, manufacturing, and quality projects, developing business processes, selecting methodologies and technologies, and improving operational efficiency. The role is fully remote and may involve AI technologies, full-stack development, and optimization modeling.

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.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Architect and maintain NetBrain’s first cloud data warehouse, ELT pipelines, transformation layer, semantic models, and BI foundation. Own data quality, governance, lineage, access controls, and curated models supporting product usage, customer adoption, account health, and go-to-market reporting. Partner with stakeholders to establish reliable metrics and a standardized source of truth, while supporting AI-assisted analytics and natural-language reporting use cases.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting identity graph construction, entity matching, and identity resolution. Partner with engineering and data science teams to productionize matching logic, evaluate match quality, monitor data quality and drift, and manage identity-provider relationships. Translate complex identity concepts into actionable insights for technical and non-technical stakeholders while helping define privacy-conscious product and data priorities.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Implements and supports security data ingestion pipelines across applications, cloud services, infrastructure, security tools, and business systems. Develops transformations, parsing, normalization, enrichment, masking, validation, and delivery workflows. Troubleshoots data quality and pipeline issues, documents architectures and data flows, coordinates with client stakeholders, and supports SIEM, detection, investigation, observability, and automation use cases. Requires data engineering experience, scripting, query languages, integration knowledge, and strong client communication.

Leads a team delivering electric utility power systems modeling and analytics. Responsibilities include transmission and distribution modeling, short-circuit and power-flow analysis, arc-flash studies, voltage imbalance analysis, thermal ampacity calculations, technical reporting, automation scripting, client management, project budgeting, scheduling, and mentoring engineers.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

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.

Own product analytics and growth measurement by partnering with product and design, defining metrics and event tracking, building dbt models and data pipelines, creating dashboards, and analyzing activation, engagement, retention, funnels, cohorts, and experiments. Establish trustworthy product data standards, identify product-market-fit indicators, surface insights, and influence roadmap decisions while collaborating with clinical and research analytics teams.

Build and scale an enterprise metrics store, dbt models, semantic-layer definitions, and analytical workflows. Develop governed business metrics, root-cause analysis, metric relationships, and reusable logic for BI tools. Partner with product, engineering, sales, underwriting, claims, and experience teams to translate business questions into actionable insights. Improve data quality, define data contracts, debug upstream and analytical issues, and help prepare metric infrastructure for API-based serving.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.

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.