Top Senior Analytics Jobs in Louisville, KY

Lead architecture and implementation of enterprise Adobe Experience Platform solutions, including data ingestion, identity resolution, XDM modeling, Web SDK event collection, and Real-Time Customer Profile. Design and operationalize customer journeys and real-time activations in Adobe Journey Optimizer, integrate AEP with downstream systems (CJA, activation platforms), lead solution design/PoCs, and collaborate with analytics, engineering, and marketing teams to build reusable frameworks and govern platform data quality.

Lead mobile analytics and experimentation for the NPR App: design and analyze experiments, extract insights from complex datasets, build reports and visualizations, write advanced SQL, partner with product and newsroom stakeholders, train colleagues, and deliver actionable recommendations to grow audience and revenue.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

The Senior Analyst, Revenue Operations is responsible for delivering analytical insights, enhancing forecasting accuracy, and optimizing revenue processes while collaborating with various departments.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.

Lead end-to-end strategic projects to optimize health plan programs, perform data-driven analyses, build impact models, present executive recommendations, advise cross-functional stakeholders, mentor junior analysts, and monitor initiative outcomes to improve efficiency and care quality.

Lead individual-contributor who builds self-service analytics, metrics, experiments and scenario simulators to detect and mitigate fraud and safety risks. Partners with product, engineering, legal and ops to operationalize metrics, run causal analyses, and support audits, incident response, and executive reporting.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Lead marketing analytics and root-cause investigations across the B2B SaaS funnel. Maintain data integrity and dashboards, monitor integrations (Marketo, Salesforce, 6Sense, Snowflake), quantify impact of trends, and recommend corrective actions. Architect and train AI/agent-driven analytics (Ask Anything), build semantic layers in Sigma/Snowflake, validate agent outputs, and partner with Revenue Marketing and Strategy teams to drive data-backed decisions and mentor junior analysts.

Lead a team of data engineers and BI developers to deliver scalable sales analytics, forecasting, and AI-enabled insights. Set strategy, prioritize roadmap, ensure platform stability and usability, translate business needs into dashboards and reporting, and partner cross-functionally to drive adoption and continuous improvement.

Perform cross-product credit analytics to inform underwriting, limits, and portfolio strategy. Build customer credit profiles, run experiments (A/B tests), uncover insights, recommend and execute policy changes, and communicate results to leadership. Leverage internal and external data, predictive techniques, and AI tools to optimize approvals, risk, and product performance while balancing growth and loss.

Build and maintain GCP-based data pipelines and foundational tables (BigQuery, Dataform, Dataplex). Merge financial and ERP datasets, define metrics, deliver reports and analyses, and collaborate cross-functionally to support decision-making and business measurement.

Design, build, and support SAP Analytics Cloud dashboards, planning models, and executive reporting. Integrate SAC with SAP and non‑SAP sources, develop KPIs and forecasting models, run testing and UAT, troubleshoot issues, and support production deployments and stakeholder workshops to deliver actionable analytics for FP&A and operational teams.

Client-facing Analytics Manager who architects large-scale digital analytics (GA4/Adobe), audits implementations, builds measurement frameworks, and leads GEO (Generative Engine Optimization) test & learn programs. Uses AI tools (Claude) to automate workflows, mentor teammates, manage projects, and translate data into strategic client recommendations.

Lead web analytics strategy and implementation: define measurement, maintain data pipelines, support 1P data integrations, troubleshoot tagging/pixel/API issues, create documentation and playbooks, translate analytics for stakeholders, and drive continuous improvement and AI-assisted automation.

Drive marketing performance measurement and attribution by building dashboards, standardizing campaign tracking across HubSpot and Salesforce, running funnel and cohort analyses, ensuring data integrity, and delivering executive reporting to connect marketing activity to pipeline and revenue.

Build, optimize, and maintain web analytics data and reporting. Deliver weekly reports and insights, perform deep-dive analyses, recommend website optimizations, partner with paid digital and email teams, and ensure accurate Adobe Analytics implementations.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

Lead and scale companywide analytics for a DTC marketing business: own marketing measurement, financial forecasting, retention/LTV modeling, data architecture, reporting, and the analytics team. Set analytics strategy, oversee data engineering roadmap, build dashboards, apply statistical and ML methods, and translate insights into executive-level decisions.

Embed with a core business unit to define problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI, or partnerships.

Own the embedded BizOps analytics function for a core business area (growth, product, AI, or partnerships). Define problems and metrics, build reports, run analyses and experiments, surface insights, make recommendations, and drive cross-functional execution to improve key business metrics.

Embedded BizOps analyst owning analytics for a core business area (growth, product, AI, or partnerships). Define problems, set metrics, build reports, run analyses and experiments, surface insights, and drive recommendations to stakeholders. Partner with product, growth, finance, and operations to ensure data-informed decisions and end-to-end execution that move key business metrics.

Own the analytics function for an embedded business unit: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Lead revenue forecasting, planning, and executive reporting across new, renewal, and expansion revenue. Manage and grow a revenue insights team while owning pre-sales analytics, forecast methodology, capacity and quota planning, and performance narratives. Build pipeline and forecast reporting, track conversion/velocity metrics, surface risks early, and apply AI to improve forecasting, anomaly detection, and automated insights.

Develop, enhance, and support Synchrony’s metadata management and cataloging platform (Ab Initio Metadata Hub). Collaborate with governance, engineering, and analytics stakeholders to ensure metadata quality, lineage, and discoverability; design solutions, implement platform changes, test, document runbooks, and enable cloud and AI/ML metadata use cases.