Top Remote Data & Analytics Jobs in Chicago, IL

Leads data stewardship and transformation for the Personal Lines Product, Pricing, and Underwriting domain. Develops data roadmaps, translates strategy into actionable epics, guides modernization to a unified insurance platform, governs data policies and compliance, and coordinates stakeholders across business and technical teams. The role requires expertise in insurance data, data modeling, warehousing, quote and policy lifecycles, premium and loss data, and technologies including Kafka, Guidewire PolicyCenter, Hadoop, and NoSQL.

Leads technical delivery of Master Data Management solutions using Informatica MDM and PowerCenter. Designs and implements ETL mappings, workflows, transformations, data quality processes, governance practices, SQL data marts, and Python automation. Oversees MDM specialists, monitors production jobs, troubleshoots operational issues, supports testing and validation, tunes performance, documents technical solutions, and collaborates with state agencies, QA, business teams, and project leadership.

Own digital media reporting and measurement by building Power BI dashboards, defining KPIs, identifying trends, designing experiments, supporting data clean room and incrementality initiatives, and writing SQL queries for cross-platform insights. Collaborate with engineering on data pipelines and tracking frameworks, then communicate actionable recommendations to technical and non-technical stakeholders.

The Manager of Regulatory Submissions will lead regulatory strategies and submissions for global authorities, ensuring regulatory alignment with business goals while collaborating with cross-functional teams on medical devices.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Design and build scalable data solutions for high-volume transaction streams. Partner with product and business teams to deliver analytics, dashboards, and insights. Lead data strategy, architecture, governance, quality, lineage, and access controls. Enable self-service capabilities and explore new open-source technologies to support business intelligence and operational needs.

Own customer data migration projects end-to-end: review, clean, map, import, validate, and QA records (events, tickets, customers, orders, donations, opt-ins, seating). Troubleshoot and monitor integrations (POS, CRM, marketing, reporting) using SQL, CSVs, APIs, webhooks, logs and admin tools. Build repeatable playbooks, documentation, and processes to reduce engineering dependency and ensure production data accuracy.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design and scale lakehouse architecture, including Bronze, Silver, and Gold data layers. Build reliable streaming and batch pipelines with Kafka, Spark, and Airflow; manage Iceberg, Delta, and Hudi table formats; optimize Trino, Starburst, and Databricks queries; and improve platform reliability, data quality, and performance. Collaborate with data scientists, analysts, and product teams to deliver scalable data platform solutions.

Build and maintain scalable ETL/ELT pipelines and data foundations using Databricks, dbt, and AWS. Transform raw structured and unstructured data into reliable datasets for product analytics and data science, including telemetry, streaming data, and feature engineering. Develop dashboards in Tableau and Databricks, optimize workflows, and collaborate with Product, Engineering, analytics, and data science stakeholders to support product health, adoption, and performance insights.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.