Top Senior Analysis Reporting Jobs in Louisville, KY

Build and maintain IICS ETL/ELT pipelines, develop Amazon Redshift data models, and use SQL and Python for data transformation and automation. Create Power BI reporting models, implement data quality checks, document pipelines, and collaborate with client stakeholders and engineering teams. The role requires autonomous work, strong communication, and the ability to translate technical solutions into business context, with bonus value for Veeva or life sciences experience.

Own board-grade analytics deliverables for client engagements by transforming Salesforce pipeline data and BI outputs into executive narratives, presentations, and reusable reporting templates. Analyze pipeline, funnel, and revenue performance; explain variance; write causal commentary; present findings to PE sponsors and executives; and train client teams to maintain deliverables independently.

Build and maintain modern data-centric applications, pipelines, transformations, enrichment processes, provisioning layers, and user interfaces supporting clinical and operational healthcare processes. Analyze clinical data and report trends using cloud, big data, data science, and modern software development practices. Collaborate with Product, Platform, and Architecture teams, mentor Data Engineers, document solutions, manage source control, and research emerging technologies.

Senior individual-contributor analyst leading end-to-end, high-stakes analyses, designing dashboards and reporting, mentoring analysts, applying statistical methods and causal inference, partnering with analytics engineering to shape warehouse models, and translating findings for technical and non-technical stakeholders.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, and Lakehouse architectures; supports SQL Server data loading and legacy C# .NET processes. Partners with analytics, software engineering, architecture, and product teams to ensure data quality and availability for reporting and machine learning. Troubleshoots pipelines and applies governance, security, compliance, documentation, and reliability best practices.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Leads the data organization across data engineering, analytics, reporting, and data science. Defines data strategy, architecture, governance, quality, and platform roadmaps; manages and develops technical teams; partners with business and engineering leaders; and guides scalable AWS-based data infrastructure, batch and real-time pipelines, and data products that support strategic growth.

Develops and maintains dashboards, reports, data tools, and data infrastructure for Consulting. Analyzes client, project, portfolio, capacity, compliance, and outcomes data to identify trends and support decisions. Maintains data integrity across Salesforce, Resource Hero, Power BI, and related systems; establishes governance standards; trains staff; and manages consulting projects, workflows, budgets, timelines, and deliverables.

Analyze and model property catastrophe reinsurance treaties using catastrophe modeling platforms. Prepare and validate exposure data, execute models, interpret risk metrics, develop scenarios, and identify portfolio risk drivers. Support underwriting, actuarial pricing, placement, renewals, and management decisions through clear reporting. Participate in model validation, calibration, research, and development while collaborating with underwriters, actuaries, and other stakeholders.