Top Senior Analytics Jobs in Milwaukee, WI

Lead development of scalable marketing data models, pipelines, and BI solutions. Partner with Marketing to define metrics, build dashboards, enable self-service analytics, and drive data governance and best practices.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Lead end-to-end funnel analytics and optimization for SMB lending, identifying friction and bottlenecks, designing measurement frameworks, running experiments (A/B testing), and partnering with Risk, Operations, Product, Engineering, and AI teams to deploy automation and AI solutions that improve conversion, cycle time, and customer outcomes while managing risk.

Lead Adobe Analytics architecture and MarTech ecosystem design, implement data layers and tagging across web and mobile, ensure QA and privacy compliance, enable reporting and audience activation, and drive cross-functional measurement and governance for personalization and optimization.

Design and implement a scalable Elasticsearch-based analytics platform integrated with an Oracle data warehouse. Architect data models, ingestion/CDC pipelines, index/shard strategies, and monitoring. Optimize cluster, queries, and caching for sub-second dashboard performance, perform load testing and capacity planning, and produce operational documentation and SLAs.

Build and maintain Sekai's analytics transformation layer: design canonical datasets, define company-wide metrics, implement data quality and freshness checks, support experimentation and cohort/funnel analysis, and partner with product and engineering to improve instrumentation and analytics tooling.

The Senior Director will establish and lead the Real-World Data & Analytics group, oversee RWD strategy, engage in complex analytics projects, and build a high-performing team in the oncology domain.

Lead Tableau data visualization strategy: design and optimize high-performance dashboards, establish governance and best practices, mentor teams, enable adoption, and partner with data teams for robust models and performance.

Lead intake, grooming, and translation of reporting and analytics requests into actionable requirements. Drive stakeholder sessions, define data logic and metrics, prioritize requests, enable scalable reporting solutions, support delivery and UAT, mentor junior analysts, and improve intake processes to reduce one-off requests and promote reusable, self-service reporting.

Lead architecture and implementation of a large-scale analytics stack: design foundational pipelines, establish modeling/orchestration standards, integrate AI-assisted engineering tools, translate business questions into version-controlled data models, partner with stakeholders to define metrics and data contracts, migrate logic into maintainable pipelines, and optimize cloud warehouse performance for scale.

Lead the RevOps analytics function: manage an analytics team, own revenue reporting and forecasting, build measurement frameworks, enforce data governance, partner with BI/engineering, analyze funnel and retention metrics, and embed AI/automation to improve revenue predictability and GTM performance.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead the Payments Insights Analytics team, utilizing data and AI/ML for strategy execution, operational excellence, and stakeholder communication.

The Senior Analytics Specialist develops processes for digital optimization and collaborates on A/B and multivariate testing through data insights.

The Data & Analytics Architect will design and implement analytics solutions, support client engagements, and lead data architecture projects, requiring extensive technical and consulting skills.

Lead analytics for law firm operations by writing advanced SQL and Python/R scripts, building executive dashboards, standardizing KPIs, analyzing case lifecycle metrics, identifying bottlenecks, and delivering actionable recommendations to drive capacity planning and process improvements.

Lead enterprise data, analytics, and AI transformation engagements as a strategic advisor and architect. Design cloud data platforms, warehouses/lakehouses, governance, integration, and BI solutions. Oversee delivery, establish standards, mentor teams, support presales, and drive measurable business outcomes across technologies like Oracle, Snowflake, Databricks, and Microsoft Fabric.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

Design, build, and maintain dbt models, ELT pipelines, Looker explores and visualizations. Drive analytics strategy, data discoverability, observability, lineage, and security. Mentor engineers, collaborate cross-functionally, apply software engineering best practices, and integrate AI to scale data products and self-service BI.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

The Senior Data Engineer will design, build, and support data environments, orchestrate pipelines, and implement data solutions for advanced analytics and machine learning, working closely with project teams.

The Analytics Engineer will design, build, and maintain scalable data models, collaborating with various teams to ensure data quality and robust reporting, mainly using dbt and SQL.

As a Director in the Data & Analytics team, you will lead client engagements, manage projects, develop strategies, create proposals, and mentor team members in data-driven initiatives.