Top Remote Analytics Manager Jobs

Lead and manage a remote analytics engineering team responsible for Extend’s Snowflake warehouse, dbt repository, ingestion pipelines, data models, monitoring, and quality standards. Oversee hiring and development, architecture, platform migrations, reliability, incident response, documentation, and self-service analytics. Partner with actuarial, risk, fraud, product, finance, and operations teams to establish trusted shared data definitions and prioritize platform improvements.

Leads advanced business analytics across Commercial, Finance, and Operations. Builds scalable solutions using SQL, Python, Snowflake, Tableau, and Power BI; performs forecasting, statistical analysis, segmentation, predictive modeling, and exploratory analysis. Develops semantic models, KPI frameworks, data governance practices, and reusable datasets while improving data quality and pipelines. Partners with leadership and technical teams to translate complex data into actionable insights, automate decision support, and influence strategic business outcomes.

Leads digital and marketing analytics for customer acquisition across multiple lending brands. Owns performance reporting, measurement frameworks, data governance, attribution and marketing effectiveness models, funnel analytics, experimentation, dashboards, and executive insights. Partners with Marketing, Risk, Finance, Operations, Technology, Engineering, and Data teams to optimize conversion, ROI, customer journeys, and data infrastructure. Requires advanced SQL, web analytics, statistical analysis, experimentation, and marketing measurement expertise.

Leads trade and distribution analytics supporting business performance, market access, contract operations, field accounts, and product launches. Responsibilities include quarterly business reviews, demand and inventory reporting, data engineering, analysis, visualization, vendor analytics, process improvement, dashboard development, launch coordination, and presentation of actionable insights. The role partners extensively across commercial, finance, compliance, legal, regulatory, supply chain, and technology teams.

Leads data engineering, business intelligence, and analytics teams; oversees data pipelines, integration infrastructure, database architecture, CI/CD practices, and enterprise data platform strategy. Partners with business and technology stakeholders to define requirements, improve data capabilities, maintain Ubuntu-based services, and deliver reporting and KPIs to leadership. Responsible for team hiring, development, mentoring, operational health, and technical execution across analytics and data integration platforms.

Own Vehlo’s customer-facing data, analytics, and insights portfolio, including unified cross-product analytics, AI-powered insights, APIs, semantic layers, benchmarking, pricing, packaging, and data governance. Lead product strategy and execution through engineering, analytics, customer success, finance, and go-to-market partners. Establish metric definitions, cost-to-serve models, customer trust measures, and premium product tiers while delivering zero-to-one products for customers ranging from small operators to sophisticated enterprises.

Leads an analytics engineering team supporting Core Data Science by building reliable data foundations, tooling, analysis pipelines, and self-service datasets. Establishes instrumentation, experimentation, documentation, data quality, and democratization practices. Partners with data scientists, engineers, product managers, designers, and researchers to turn insights into product impact. Requires expertise in large-scale data manipulation, SQL, Python or R, ETL/ELT, workflow orchestration, and AI-assisted analysis, along with engineering management experience.

Leads logistics analytics and optimization across distribution and contact center networks. Develops analytical models, machine learning algorithms, automated processes, forecasting solutions, and scalable optimization tools. Partners with operations, engineering, and technology teams to improve productivity, capacity, service, cost, and decision-making. Deploys and monitors machine learning models in production, measures business impact, and recommends continuous improvements.

Lead and optimize new-customer onboarding to accelerate time-to-value. Instrument product flows, define and track activation metrics, run experiments, and translate behavioral insights into a prioritized roadmap. Collaborate with customer success, sales, support, data, and engineering to ship, measure, and iterate on features end-to-end.

Serves as a strategic analytics partner to Marketing, owning funnel, attribution, ROI, account scoring, experimentation, and pipeline analytics. Builds scalable datasets, dashboards, and quality controls while connecting customer, campaign, product usage, CRM, and revenue data. Delivers executive-ready reporting, QBR insights, and recommendations that improve conversion, marketing investment effectiveness, account-based marketing, product adoption, and pipeline growth.

Owns the health of a multi-client digital tracking ecosystem across 100+ properties. Responsibilities include managing Piwik PRO and Google Tag Manager configurations, deploying pixels and telemetry updates, monitoring and debugging conversion events, validating implementations with browser tools and APIs, supporting technical client onboarding, managing consent and UTM governance, documenting configurations, and coordinating technical escalations. The role also explains tracking behavior to nontechnical teams and improves automation and monitoring processes.

Lead a GTM Analytics team supporting Marketing, Sales, RevOps, and Customer Operations. Manage embedded analysts, establish trusted reporting and QA processes, review SQL and dashboards, guide funnel, campaign, sales, support, forecasting, segmentation, attribution, and experimentation analysis, and deliver executive recommendations. Partner with GTM leaders and data engineers, coach analysts, and develop self-service analytics, automation, governed dashboards, metric documentation, and AI-assisted workflows.

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Manages an HR Analytics team conducting advanced analysis and modeling of HR, payroll, financial, and business data. Leads dashboard and reporting development, translates business questions into analytical solutions, provides strategic recommendations, manages analytical projects, and supports change management. Develops team capabilities through hiring, coaching, mentoring, and performance management while partnering with HR stakeholders to improve decision-making and business outcomes.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Leads Workday HRIS administration, system enhancements, integrations, reporting, dashboards, data governance, and workforce analytics. Partners with HR, IT, Finance, Payroll, Benefits, Talent Acquisition, and business leaders to improve HR technology, data quality, employee experience, and decision-making. Manages complex implementations and process improvement initiatives while promoting self-service analytics and continuous optimization.

Leads Workday HRIS administration, system enhancements, integrations, reporting, dashboards, data governance, and workforce analytics. Partners with HR, IT, Finance, Payroll, Benefits, and Talent Acquisition stakeholders to improve HR technology, data quality, employee experience, and decision-making. Manages complex HR technology implementations and process improvements while promoting self-service reporting and continuous optimization.

Leads enterprise architecture for data, analytics, and AI/ML platforms. Defines cloud, Lakehouse, streaming, governance, integration, and machine-learning architecture standards; guides modernization, proof of concepts, platform scalability, security, and cost optimization. Partners with executives, product owners, cybersecurity, governance, and engineering teams while leading design reviews, architecture forums, and technical programs. Requires deep experience with Databricks, cloud-native data platforms, distributed systems, data governance, and enterprise-scale architecture.

Leads and develops a data engineering team supporting People Analytics and AI-assisted workforce analytics. Designs scalable data models, pipelines, semantic layers, testing frameworks, and governance practices. Remains hands-on with production code, reviews, and technical problem-solving while partnering with People Analytics, business, legal, and compliance stakeholders. Establishes engineering standards across Python, SQL, dbt, Airflow, Snowflake, CI/CD, data quality, observability, privacy, and security.

Leads medium- to large-scale analytics projects and client relationships while conducting data analysis, managing data infrastructure, building reports and calculators, and preparing client presentations. Provides strategic insights, oversees deadlines and quality, mentors analysts, collaborates with data science and technical teams, and communicates findings to technical and non-technical stakeholders.

Own client-facing analytics strategy and implementation across GA4, Adobe Analytics, tagging, measurement frameworks, dashboards, experimentation, and GEO measurement. Audit and QA analytics implementations, define KPIs, evaluate incrementality and causal relationships, present executive-ready insights, and collaborate with SEO, paid media, creative, data, engineering, and client teams. Build AI-assisted workflows, automations, scalable tools, and dashboards while mentoring teammates and managing multiple client workstreams.

Drives new business growth in mobility data and analytics markets by identifying opportunities, developing strategic plans, building partner and reseller relationships, negotiating cooperative contracts, and managing sales pipelines. Conducts market and competitor analysis, supports RFX processes, develops sales targets and budgets, and collaborates with internal teams and leadership. Represents the company at trade shows and conferences while serving as a subject matter expert for partners and resellers.

Own the strategy, roadmap, and delivery of RevenueCat’s analytics products, including charts, benchmarking, dashboards, and predictive LTV. Investigate complex data issues, develop metrics and benchmarks, define product vision, and collaborate with engineering, data science, design, leadership, and go-to-market teams. The role requires strong product management experience, analytical and technical fluency, customer empathy, data visualization expertise, and the ability to make independent product decisions in a remote environment.

Lead the vision, roadmap, discovery, development, and launch of AI-enabled analytics products for nursing and medical education. Translate customer and business needs into product strategies, prioritize backlogs, align cross-functional teams, define success metrics, and use feedback and data to improve outcomes. The role requires responsible AI practices emphasizing quality, privacy, transparency, and trust.

Lead the vision, roadmap, discovery, and delivery of AI-enabled analytics products for nursing and medical education. Translate customer and business needs into product strategies, prioritize backlogs, guide cross-functional teams, and measure adoption, usability, customer impact, and business outcomes. Champion responsible AI practices involving quality, privacy, transparency, and trust while using customer feedback and product data to continuously improve products.