Top Data & Analytics Jobs in Hartford, CT

Designs and maintains data warehouses, semantic models, metrics layers, pipelines, dashboards, and certified datasets. Establishes KPI governance, data security, row-level access controls, quality standards, and observability. Builds Sigma, Power BI, and Databricks dashboards, supports self-service analytics, resolves BI issues, and enables AI/ML use cases. Collaborates with architecture, engineering, analysts, and business stakeholders to define requirements, document solutions, train users, and drive BI adoption.

Own and enhance Sigma dashboards and business intelligence reporting for the Records Retrieval business. Partner with stakeholders to translate business needs into scalable data solutions, analyze complex datasets, improve data quality and governance, define reporting standards, and support Snowflake data warehouse and modeling initiatives. Deliver actionable insights, automate reporting processes, evaluate analytics tools, and independently drive analytics projects from requirements through delivery.

Loads, queries, extracts, validates, and refreshes client data; prepares reports and deliverables; identifies automation and process-improvement opportunities; supports UAT, system implementations, and quality assurance testing; and collaborates with cross-functional teams. The role translates client requirements into actionable tasks and requires SQL, relational database, Oracle, Unix, data analysis, reporting, and communication skills.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and operate trusted data products, governed datasets, scalable pipelines, and platform workflows supporting analytics, AI, and business operations. Responsibilities include data quality, validation, observability, recoverability, semantic consistency, governance controls, permissions, privacy-aware access, automation, and production monitoring. The role partners with stakeholders to resolve ambiguous data needs, drives platform improvements, provides technical direction, and adopts agentic development workflows.

Own healthcare analytics data methodology, including episode grouping, provider attribution, cost standardization, quality measures, reference data, pipelines, QA, and production delivery. Translate feature requests into specifications and validated analytics. Reconcile results, establish recurring data-quality coverage, investigate failures, and collaborate with engineering, data science, product, clinical informatics, and customer teams. Advanced SQL and healthcare claims expertise are central to ensuring accurate, defensible customer metrics.

Own analytics for Customer Success and Operations by translating ambiguous business questions into metrics, reports, dashboards, and PRDs. Partner with senior leaders, CSMs, Analytics Engineers, and Operations teams to improve retention, permit turnaround, and organizational scalability. Build self-service dashboards, develop dbt models, document analytics assets, prioritize high-impact requests, and use LLM tools to accelerate SQL, reporting, and data-model development.

Build and maintain Snowflake and dbt data platforms, including ELT pipelines, tested models, orchestration, monitoring, documentation, access controls, and governance. Optimize performance and costs while ensuring reliable, discoverable data for internal dashboards, client products, and AI/ML use cases. Translate ambiguous stakeholder needs into durable solutions and apply strong software engineering practices, including Git, CI/CD, code review, Python, testing, and alerting.

Establish and operationalize Finance data governance across Finance and Technology. Define governance models, business glossaries, data inventories, lineage, quality controls, master-data standards, hierarchies, and reconciliation processes. Support SAP, Snowflake, reporting, planning, and analytics data products; facilitate stakeholder workshops; document requirements; manage governance backlogs; and coach data owners and stewards in an ambiguous enterprise environment.