Top Remote Analytics Engineer Jobs

Own Eve’s go-to-market data models and semantic layer for governed metrics including pipeline, conversion, CAC, win rate, ARR, and bookings. Establish certification frameworks, modeling standards, documentation, alerting, and analytical application patterns across the organization. Partner with Sales, Marketing, Customer Success, and RevOps stakeholders, resolve conflicting metric definitions, enable AI-grounded data tools, review contributions, and mentor analytics engineers.

Architects the data and AI foundation for marketing, sales, and analytics. Responsibilities include building Databricks pipelines, Gold Layer tables, semantic models, documentation, AI-enabled data products, and production LLM or agent systems. The role leads cross-functional initiatives, translates ambiguous business needs into scalable solutions, ensures data quality and reliability, mentors engineers, automates workflows, and partners with BI, data science, marketing, sales, and R&D teams.

Own Hometap’s product and marketing event-data ecosystem, including taxonomy, tracking plans, governance, Segment Protocols, schema validation, and source management. Partner with marketing, product, and engineering to define instrumentation and event specifications. Build monitoring for schema conformance, event volume, and property completeness, while documenting standards and managing event review and deprecation processes.

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.

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.

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 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.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and documentation for self-service analytics. Lead cross-functional data initiatives, ensure reliability and quality, mentor engineers, and build production LLM and agent systems. Translate ambiguous business needs into scalable solutions, automate marketing and go-to-market workflows with AI, and deliver high-quality Python and SQL while overseeing AI-generated code.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and ETL/ELT workflows. Build trusted data products for analytics, automation, and AI agents; translate ambiguous business needs into scalable solutions; lead cross-team initiatives; ensure reliability and data quality; mentor engineers and analysts; and develop, evaluate, and monitor production LLM and agent systems using Python and SQL.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, ensure data quality and reliability, and mentor engineers and analysts. Build, evaluate, and monitor production LLM and agent systems, automate marketing workflows with AI, and deliver high-quality Python and SQL solutions supporting Marketing, Sales, and R&D.

Architects Samsara’s marketing data and AI foundation, including Databricks pipelines, Gold Layer tables, semantic models, and documentation for self-service analytics. Leads complex cross-functional data initiatives, translates ambiguous business needs into scalable solutions, mentors engineers, and ensures data reliability and quality. Builds and evaluates production LLM and agent systems, automates marketing and go-to-market workflows with AI, and delivers high-quality Python and SQL while supporting sales, marketing, and product teams.

Designs scalable ETL/ELT pipelines, analytics-ready data models, and executive dashboards using Python, SQL, Tableau, Power BI, and cloud tools. Performs data mining, modeling, validation, documentation, and reporting while collaborating with technical and business stakeholders. Provides mentorship and technical guidance on analytics engineering, workflow automation, and data best practices. The role is fully remote, requires U.S. citizenship, and requires or permits obtaining Public Trust and Top Secret eligibility.

Designs scalable data solutions, layered data models, semantic layers, and curated business-ready datasets. Partners with stakeholders, BI, IT, and Data Engineering to translate requirements into trusted analytics solutions. Owns ELT modeling, KPI standards, data quality checks, Tableau data sources, and advanced Tableau support. Requires strong SQL, Snowflake, dimensional modeling, Tableau, documentation, version control, and stakeholder communication skills.

Build and maintain SQL and dbt data models, tables, views, dictionaries, and self-service tools on BigQuery. Support Accounting, Actuarial, and Claims stakeholders from request through production, monitor data quality, prevent defects, document processes, and review AI-generated code. The role requires hands-on data development, collaboration across projects, and growing ownership of a data domain and stakeholder relationships.

Build and migrate production data models from Alteryx to dbt, manage Dagster or Airflow pipelines in Redshift, and create Tableau insights for Revenue Operations. Partner with business stakeholders to translate data into strategy and ROI. Explore LLMs, predictive analytics, and SageMaker machine learning applications such as churn prediction and lead scoring. Support ABM, customer health scoring, and globally aligned KPI reporting.

Build and maintain dbt data models while migrating workflows from Alteryx, orchestrating pipelines with Dagster or Airflow, and managing Redshift data warehouses. Partner with RevOps stakeholders to translate data into business insights and Tableau dashboards. Explore AI, LLM, predictive analytics, and SageMaker machine learning applications for churn prediction, lead scoring, ABM, and customer health scoring. Ensure data quality, documentation, testing, reliability, and measurable business impact.

Build and maintain production data models while migrating workflows from Alteryx to dbt. Manage Redshift pipelines using Dagster or Airflow, create Tableau dashboards, and translate technical data into business insights for Revenue Operations. Collaborate on AI, predictive analytics, churn prediction, and lead scoring initiatives using LLMs and Amazon SageMaker. Serve as a liaison between data teams and business stakeholders across Sales, Marketing, and Customer Experience.

Build and maintain production data models while migrating workflows from Alteryx to dbt. Manage Redshift pipelines with Dagster or Airflow, create Tableau dashboards, and translate technical data into business insights for Revenue Operations. Explore AI, LLMs, predictive analytics, and SageMaker use cases such as churn prediction and lead scoring. Collaborate with Sales, Marketing, Customer Experience, Product, and Engineering stakeholders.

Build and maintain dbt-based data transformations, analytics models, ELT workflows, testing, and data quality processes. Collaborate with analysts, engineers, and stakeholders to support reporting, self-service exploration, governance, and business decision-making. Troubleshoot data issues, document workflows, evaluate analytics tools, and contribute to scalable data practices using a modern cloud data stack.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Own Step’s analytics data models, transformation pipelines, metric definitions, semantic layer, and data quality practices. Partner with Product, Marketing, Engineering, Risk, Growth, and Finance to establish trustworthy data foundations and enable self-service analysis. Build reusable query, visualization, and code components; apply AI-augmented workflows; and collaborate with data scientists, analysts, and platform engineers. The role requires strong SQL, modeling, pipeline, BI, communication, and independent ownership skills, with occasional travel for team onsites.

Own the analytics product vision, roadmap, prioritization, and delivery across Clinical Solutions. Lead customer-facing and internal dashboards, KPIs, embedded insights, data quality, governance, and generative AI applications. Partner with technology, informatics, analytics, commercial, sales, marketing, and customer success teams to deliver measurable clinical and business outcomes. Represent analytics priorities, drive adoption, and monitor healthcare technology, regulatory, and industry trends.

Provides technical leadership for enterprise analytics by architecting SQL-based transformations, scalable data models, semantic layers, and governed datasets. Builds analytics products supporting healthcare KPIs, patient journeys, scheduling, retention, and operational decisions. Establishes analytics engineering standards, testing, documentation, lineage, and code-review practices; troubleshoots data-quality issues; partners with business and technical stakeholders; and mentors analysts in SQL, dimensional modeling, dbt, and governed data-product development.

Owns the enterprise Snowflake data platform end to end, including architecture, ingestion, transformation, security, governance, reliability, performance, cost optimization, and AI enablement. Establishes engineering standards, reviews architecture, resolves complex production issues, mentors data professionals, and leads cross-functional adoption of scalable, governed analytics capabilities. The role provides senior technical influence without direct people management and supports secure MCP-based AI integrations.

Own the analytics product vision, roadmap, prioritization, and delivery across Clinical Solutions. Develop customer-facing and internal dashboards, KPIs, embedded insights, and generative AI capabilities that improve healthcare decision-making. Translate strategy into execution with technology, informatics, analytics, commercial, and customer success teams. Define product outcomes, ensure data quality and governance, support commercialization and adoption, and monitor healthcare technology and regulatory trends.