Top Senior Analytics Jobs in Louisville, KY

Own marketing measurement from initial engagement through closed-won revenue. Manage cross-channel reporting tied to Salesforce pipeline, tracking across GA4, advertising platforms, SEO, and AI search. Validate multi-touch attribution, analyze marketing experiments, model budget impact, and recommend investment priorities. Present performance insights to marketing and sales leadership across global regions.

Own Toast’s Voice of the Customer program across customer segments, analyzing NPS, CSAT, CES, qualitative feedback, and business metrics. Identify root causes of customer pain, develop data-driven recommendations, improve CX measurement practices, and influence cross-functional teams to prioritize customer-focused solutions. The role also builds listening channels, promotes customer-centric decision-making, and aligns CX priorities with organizational goals.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

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.

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.

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.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Build customer health scores, churn and expansion analytics, retention dashboards, automated alerts, and executive reports. Maintain integrations among databases, CRM, and BI tools while improving data quality and standardizing reporting. Lead AI adoption for analytics, validate generated insights, and enable customer-facing teams to make informed retention and expansion decisions. Requires strong BI, logistics or supply chain analytics, dashboarding, data integration, communication, and customer-focused skills.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Evaluates AI/LLM-powered product quality, model and product performance, customer behavior, usage, and feature adoption. Defines KPIs and success metrics, develops evaluation frameworks, identifies instrumentation gaps, supports experimentation and analytics infrastructure, and creates dashboards and reports. Partners with Product, Engineering, Data Science, and Business teams to translate analytical insights into product improvements.

Leads and transforms a sales performance analytics organization into a strategic, insight-driven business partner. Oversees analytics products, dashboards, forecasts, KPIs, governance, adoption, data quality, and responsible AI initiatives. Partners with senior sales, business, and technical leaders to rationalize reporting portfolios, prioritize high-value analytics, guide technical delivery, and translate complex data into actionable decisions. Coaches a multidisciplinary team and leads organizational change across a complex commercial environment.

Develop customer analytics that improve retention, growth, adoption, and operational performance. Build and maintain BI dashboards, customer health metrics, churn-risk reporting, and predictive models. Partner with Client Success, Product, Sales, and Operations to translate complex findings into actionable recommendations. The role also owns data quality, governance, reporting automation, workflow improvements, and stakeholder communication.

Leads digital measurement analytics for marketing platforms, including data collection and validation, dashboard development, feature performance reporting, user behavior analysis, journey friction identification, and UX improvement recommendations. Partners with marketing, technology, digital, and analytics stakeholders to deliver strategic priorities, manage projects, communicate insights, and build analytics roadmaps using tools such as Adobe or Google Analytics, Python, SAS, SQL, Tableau, and AWS SageMaker.

Analyzes and forecasts financial, economic, operational, and claims data to support strategic decisions. Develops reporting, metrics, models, and data controls; investigates anomalies; monitors claims trends; compares actual experience with forecasts and pricing assumptions; and translates complex findings into recommendations. Provides advanced actuarial guidance to business teams and senior executives, supports Medicare Individual analytics, and helps drive enterprise-wide strategies.

Develop and modernize Tableau dashboards for People Analytics, improving visualization, usability, performance, and self-service insights. Design and maintain Tableau row-level security for sensitive employee data, including dynamic access controls and entitlement mappings. Build optimized data sources and models using Snowflake and SQL, troubleshoot data and integration issues, document solutions, and collaborate with technical and business stakeholders on scalable analytics products.

Own the analytics engineering roadmap, architecture, and production data models. Build and optimize dbt models using SQL, Python, and Databricks; establish dimensional models, governed metrics, testing, documentation, lineage, monitoring, and CI/CD practices. Partner with data engineering, analysts, BI developers, and stakeholders to deliver reliable analytics products, improve performance and cost efficiency, investigate data quality issues, and mentor team members.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.

Manages client, compliance, and performance reporting across healthcare programs using eligibility and claims data. Leads reporting analysts while remaining hands-on with advanced SQL, dashboards, data crosswalks, validation, reconciliation, root-cause analysis, and data-quality monitoring. Partners with data engineering and clients to resolve upstream issues, establish reporting standards, and guide the analytics roadmap.

Leads marketing campaign measurement and analytics to evaluate campaign effectiveness, optimize performance, and maximize ROMI. Uses SQL, SAS, visualization tools, and advanced analytics to deliver actionable recommendations, present insights to senior stakeholders, drive adoption of measurement tools, manage projects, and mentor team members. Partners cross-functionally with marketing, analytics, and finance teams across geographies.

Lead GTM analytics across performance measurement, channel economics, data governance, forecasting, planning, and executive reporting. Own metric definitions, data standards, analytical priorities, and business requirements while partnering with Marketing, Data Science, Finance, RevOps, and Engineering. Analyze funnel, attribution, CAC, payback, conversion, retention, pipeline, and territory performance, translating findings into investment recommendations and strategic decisions for senior leadership.