Top Senior Analysis Reporting Jobs in Hartford, CT

Designs and maintains enterprise data pipelines, warehouses, lakes, semantic models, ETL/ELT processes, and SQL solutions. Supports Power BI datasets and reporting architectures while ensuring data quality, governance, security, and performance. Collaborates with HR stakeholders, analysts, data scientists, and technology teams to translate business requirements into scalable technical solutions. The role also contributes to data architecture, troubleshooting, documentation, and engineering best practices.

Owns retention analytics for a new Loyalty & Retention organization, including churn definitions, survival analysis, forecasting, experimentation, acquisition attribution, satisfaction analysis, payment recovery, and save-desk performance. The role builds reporting and predictive models from scratch, partners with BI and actuarial teams, influences cross-functional decisions, and presents retention findings and forecasts to executives.

Lead global data governance strategy and operating model, establish enterprise data management/MDM and data certification practices, build and lead the global governance function and steward program, drive change management and adoption across business lines, partner with cross-functional teams to resolve data risks and embed governance into workflows, and deliver measurable outcomes against the governance roadmap.

Own end-to-end analytics projects and data products for internal and external stakeholders. Develop advanced SQL transformations, maintain reliable near-real-time datasets, investigate metric changes, deliver actionable recommendations, and mentor junior analysts across cross-functional initiatives.

Designs and governs enterprise data architecture supporting VHA EHR modernization, reporting, analytics, clinical systems, and data integration. Responsibilities include data modeling, governance, metadata and lineage management, APIs, ETL, interoperability, data quality, cloud infrastructure, performance optimization, and stakeholder collaboration. The role also leads architectural enhancements through Agile processes and evaluates emerging data technologies.

Build and operate production-grade data pipelines and models connecting marketing, event, operational, and SaaS data into Databricks. Own data quality, testing, monitoring, lineage, orchestration, attribution, identity resolution, and governed semantic-layer contributions. Partner cross-functionally on privacy-aware data flows, event taxonomies, and reliable Growth data products while improving engineering standards and maintainability.

Leads Versapay’s enterprise data strategy, architecture, governance, quality, accessibility, and commercialization. The role unifies transactional and analytical data, establishes semantic models and data catalogs, enables secure AI and agentic capabilities, develops external data products, manages data platform costs, and leads teams across data engineering, analytics, BI, and platform operations. The Senior Director partners with executive, product, engineering, commercial, finance, legal, and compliance stakeholders.

Analyzes web, customer, and campaign data to generate actionable digital marketing insights. Builds dashboards and presentations using Adobe Analytics, Power BI, or Tableau; evaluates site performance, media monetization, A/B tests, and web projects; supports tagging, reporting validation, and vendor implementations. The role partners with stakeholders and Agile teams, manages analytics priorities, and communicates findings to guide marketing strategy and business decisions.

Develop and maintain large-scale, cloud-native data services and distributed pipelines for multi-tenant healthcare systems. Responsibilities include data modeling, database component development, SQL programming, performance tuning, testing, deployment, code reviews, proof-of-concepts, and full software development lifecycle ownership. Collaborate with global engineering teams, evaluate technologies, apply security and operational requirements, and use AI tools to accelerate development.

Leads the design, automation, optimization, and maintenance of scalable data pipelines using Airflow, dbt, Python, SQL, and Google Cloud technologies. Manages dbt models, develops reusable architecture patterns, monitors pipeline performance, supports CI/CD and testing, and ensures data security and governance. The role also provides technical leadership, mentors engineers, collaborates cross-functionally, documents implementations, and drives continuous improvement across data engineering practices.