Top Senior Data & Analytics Jobs in Hartford, CT

Design, support, and improve data analytics solutions (OAC and Power BI). Manage data ingestion, ETL, data quality, access, and documentation. Liaise with stakeholders, migrate dashboards from OAC to Power BI, and follow SDLC and project management practices.

Build and maintain Quince's compensation data environment: integrate HR systems, automate benchmarking and AI workflows, support comp reviews and equity approvals, analyze pay trends, and partner with Talent, Finance, and Legal on global comp strategy.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Lead end-to-end customer engagements to architect, implement, and optimize large-scale Spark/Databricks workloads. Troubleshoot and tune PySpark and DBSQL jobs, design PoCs and workshops, advocate strategically for customers, collaborate cross-functionally, and mentor junior engineers on Lakehouse best practices.

Lead architecture, implementation, and optimization of scalable data platforms, ETL/ELT pipelines, and data governance. Ensure security and compliance, mentor junior engineers, drive CI/CD and IaC for data workflows, and collaborate with stakeholders to deliver actionable analytics and reporting.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Build and operate ingestion and transformation pipelines, model and test core warehouse data, enable analytics and reporting, and maintain ML data infrastructure feeding matching and recommendation models. Partner with Engineering, Product, and Data Science to ensure reliable, documented, and trusted data at scale.

Design and own capital and catastrophe modeling frameworks to evaluate policy- and portfolio-level risk and capital. Run and integrate catastrophe model outputs, build analytical tools and data structures, assess reinsurance impacts, and translate results into actionable recommendations for underwriting, finance, and portfolio strategy.

Provide advanced technical support and subject-matter expertise for Relativity platforms (RelativityOne/Server), design and maintain complex workspaces and permissions, configure analytics/processing/imaging, develop event handlers and advanced search logic, deliver client-facing training/demos, mentor junior analysts, review deliverables, and maintain eDiscovery SOPs and best practices.

The Databricks Architect leads data engineering solutions, designing scalable Databricks platforms and managing cloud implementations. They work with clients, ensuring technical alignment and project execution.