Top Remote Analysis Reporting Jobs in Madison, WI

The Senior Data Analyst will lead metrics definition, reporting, statistical analysis, experimentation, dashboard management, and pricing analytics. The role involves building data pipelines, collaborating with data engineers on data assets and feature stores, investigating complex business questions, and translating analytical findings into strategic recommendations for product, pricing, and senior leadership. Strong SQL, statistics, econometrics, communication, and cross-functional collaboration skills are essential.

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.

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.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

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.

Leads global contact center capacity planning, forecasting, headcount, and hiring strategies. Builds predictive staffing models using Python, analyzes contact flows and staffing accuracy, and develops reports with MicroStrategy, Alteryx, and Power BI. Communicates data-driven recommendations to executives, partner managers, vendors, and operations teams while managing planning initiatives and optimizing service levels, customer experience, and costs.

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.

Lead design and implementation of scalable Databricks Lakehouse solutions using Delta Lake, Unity Catalog, and MLflow. Advise stakeholders, drive cloud data platform modernization across Azure/AWS/GCP, ensure data governance and quality, optimize performance and cost, and mentor teams. Support presales, deliver billable consulting, and champion AI/ML-enabled data solutions to translate platform capabilities into business outcomes.