Top Remote Data & Analytics Jobs in Madison, WI

Provides senior-level data analysis, compliance reporting, qualitative and quantitative research, business intelligence, dashboards, and program measurement for the Personal Financial Counselor financial disclosure and conflict-of-interest program. Tracks certification, readiness, onboarding, and workforce metrics; identifies trends and risks; develops executive reports and actionable recommendations; supports audit readiness, process improvement, and cross-functional program decision-making.

Serves as the liaison between business stakeholders and ServiceNow developers, gathering requirements and mapping business processes to workflows. The role supports dashboards, KPIs, performance analytics, data validation, cleansing, transformation, testing, UAT, documentation, data governance, compliance, training, and process optimization. It develops user stories, acceptance criteria, and test scripts while helping federal agencies improve ServiceNow reporting, workflow automation, and operational performance.

Build and optimize data transformation pipelines using SQL, dbt, Airflow, Python, BigQuery, and relational databases. Collaborate with stakeholders and data engineers, manage task scheduling, resolve data issues, perform analyses, present recommendations, support data exploration tool adoption, and mentor team members. The role also contributes to hiring, onboarding, and professional development while ensuring data accuracy and insight quality.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects through continuous improvement.

Designs and maintains production data ingestion pipelines, layered analytics-ready data models, and reliable automated data foundations. The role integrates client data from APIs, files, Databricks, and shared-data sources; implements testing, monitoring, schema-change handling, and safe reprocessing; and supports application, analytics, and future AI use cases. It also requires collaboration with product teams, strong communication, cloud data warehouse experience, and AI-native development practices.

Designs and maintains secure, reliable data platforms, including relational data models, migrations, pipelines, monitoring, data quality processes, and self-service reporting access. The role protects PHI and PII through access controls, encryption, audit logging, and governance in a HIPAA environment. It also involves backend API development, code reviews, security reviews, stakeholder collaboration, and mentoring teams on data practices.

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

Analyze behavioral, transactional, and interaction data to identify fraud patterns and emerging threats. Develop monitoring frameworks, dashboards, and mitigation solutions; evaluate AI fraud-review decisions; and translate findings into prompt, rule, workflow, and machine-learning feature improvements. Collaborate with Fraud Operations and Machine Learning teams, participate in code reviews, and mentor junior analysts. The role requires strong SQL and Python skills, statistical analysis expertise, business judgment, and fraud analytics experience.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.