Top Analytics Jobs in Memphis, TN

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Leads analytics strategy and technical delivery for clients, specializing in Adobe Analytics and Customer Journey Analytics. Conducts measurement workshops, defines KPI and implementation strategies, oversees tagging and data quality, guides experimentation, and reviews dashboards and insights. Advises senior stakeholders, manages and mentors consultants, supports proposals and account growth, and contributes to Adobe Experience Platform initiatives involving Target, RT-CDP, and AJO.

Leads Toast’s analytics engineering function, owning enterprise data architecture, modeling strategy, analytics-layer development, and AI-ready semantic-layer vision. Builds and manages a team of analytics engineers, establishes roadmaps, partners with data engineering and business leaders, and drives scalable, governed data solutions across domains. Requires expertise in data modeling, dbt, SQL, stakeholder alignment, and people leadership.

Lead programmatic analytics for clients and internal teams by analyzing campaign and model performance, designing tests, developing measurement solutions, recommending optimizations, and presenting actionable insights. Partner with Sales, Customer Success, Product, Engineering, and clients to align marketing strategies, reporting requirements, and optimization efforts with business goals.

Leads the strategy, architecture, implementation, and evolution of enterprise analytics platforms. Responsibilities include designing scalable data platforms, integrating digital and enterprise data, establishing governance and security standards, optimizing analytics and BI capabilities, evaluating cloud technologies, managing platform performance and risks, and mentoring technical teams. The role also requires close collaboration with executives, clients, Product, Engineering, Data Science, Marketing, and business stakeholders.

Lead and scale Runpod’s analytics and business intelligence function, managing analysts and BI practitioners while setting the analytics roadmap. Own core company metrics, consumption and marketplace analytics, self-service reporting, experimentation practices, and executive and board-level insights. Partner with Product, Finance, Engineering, Security, Supply, and Go-to-Market leaders to improve data quality, modeling, decision-making, and strategic outcomes.

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.

Leads analytics for Pricing and Supply, managing a team of approximately eight analysts. Partners with cross-functional leaders to prioritize analytical work, build dashboards and decision frameworks, improve metric quality, and generate insights into pricing, supplier coverage, marketplace performance, conversion, margin, and customer behavior. Coaches analysts, establishes scalable analytics processes, and collaborates with data engineering teams to improve data models, tooling, and self-service capabilities.

Leads a 15+ person analytics organization supporting Growth, Sales, Marketing, and Finance. Owns the analytics strategy, operating model, metric governance, executive reporting, forecasting, experimentation, customer and revenue insights, and data foundation improvements. Partners with senior commercial and financial leaders to translate complex data into strategic recommendations and builds scalable processes, systems, and talent development programs.

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 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.

Develop and maintain dashboards, reports, scorecards, KPI solutions, and analytical models that provide business performance insights. Responsibilities include trend and root-cause analysis, forecasting, data validation, stakeholder requirements gathering, reporting automation, process improvement, documentation, and data quality governance. The role partners with cross-functional teams and communicates findings to technical and non-technical audiences.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Supports BSA/AML transaction monitoring, financial crime analytics, risk reporting, and business intelligence. Develops dashboards, KPIs, KRIs, visualizations, executive summaries, and automated reporting solutions. Performs data validation, mapping, statistical and root cause analysis, and assists with data modeling. Maintains documentation and audit evidence, supports testing and fintech partner implementations, and identifies process and reporting improvements.

Lead revenue analytics by developing business cases, dashboards, KPI frameworks, experiments, and automated analytics solutions. Partner with client success, marketplace, merchants, and cross-functional teams to evaluate performance, guide go-to-market decisions, quantify product value, and scale data-driven execution. Serve as the analytics subject matter expert while translating structured and unstructured data into actionable recommendations and compelling stakeholder narratives.

Lead end-to-end analytics for major platform partnerships, identifying growth opportunities and informing product, investment, risk, and pricing decisions. Conduct complex analyses, design experiments, build scalable data models, metrics, dashboards, and AI-assisted workflows, and translate findings into business recommendations. Partner cross-functionally with Product, Engineering, Finance, Revenue, and Strategic Partnerships while mentoring analysts and improving analytical practices.

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 a multidisciplinary marketing analytics organization spanning data engineering, analytics engineering, visualization, and data science. Builds attribution, ROI, customer journey, predictive signal, data governance, and AI-ready information architecture systems. Partners with Sales, RevOps, Finance, PLG, and Data teams to improve capital allocation and growth decisions. Oversees executive dashboards, metric alignment, team capacity, and strategic analytics delivery.

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 analytics, reporting, data modeling, automation, AI-agent development, and quality assurance for revenue cycle operations. Creates Power BI and Excel reports, analyzes complex healthcare and operational datasets, develops scalable warehouse models, and delivers actionable insights to leadership. Uses SQL, cloud data platforms, Git, Python, and AI-assisted tools to improve reporting efficiency, accuracy, and decision support.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.

Leads Planning Analytics and Rapid Analytics teams supporting segmentation, quota planning, territory models, investment optimization, and executive decision-making. Builds rapid, hypothesis-driven analyses and translates complex data into actionable recommendations. Partners with Engineering to develop scalable data foundations, semantic models, governance, self-service analytics, and AI-ready capabilities using Power BI, Fabric, and SQL. Oversees talent development, stakeholder alignment, and strategic analytics consulting across business functions.

Build and steward Netflix advertising quality-of-experience data, from device telemetry and instrumentation through data pipelines and dashboards. Collaborate with product engineering, data engineering, and data science teams to measure user-perceived performance, improve data quality, document user flows, and establish monitoring and measurement best practices. The role requires strong SQL and Python skills, technical communication, stakeholder management, and comfort working independently in an ambiguous environment.

Leads analytics and data engineering for Sales and Revenue Operations. Builds reporting in Looker and Snowflake, manages CRM data flows into cloud warehouses, establishes transformation and modeling layers, and develops forecasts, scoring, churn, and expansion models. Supports territory, quota, pricing, and revenue-truth initiatives while presenting insights to finance and company leadership.

Analyze product, user, transactional, and repayment data to develop risk and underwriting models, reduce losses and fraud, monitor model performance, and guide product and growth strategy. Build Looker dashboards and LookML models, conduct segmentation and experimentation, measure CAC and LTV, and communicate actionable insights to product, engineering, marketing, finance, and leadership.