Top Data & Analytics Jobs in Hartford, CT

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

Leads Marketing's data architecture, metric governance, analytical modeling, predictive modeling, experimentation, and executive performance reporting. Manages three to four analysts while remaining hands-on with advanced SQL, Snowflake data validation, dimensional modeling, cross-system reconciliation, attribution, customer analytics, and AI-assisted natural-language analytics. Partners with engineering, governance, product, and marketing teams to define data products, ensure analytical accuracy, and establish scalable measurement standards.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.

The Data Governance Analyst administers data governance platforms and processes, including enterprise data catalogs, Databricks Unity Catalog, metadata, lineage, data stewardship, MDM, privacy compliance, and data quality initiatives. The role leads catalog roadmaps, supports Osano, coordinates with data owners and engineering teams, monitors governance adoption and metrics, and develops scalable governance standards and procedures.

Leads the full digital experience research lifecycle, including user interviews, testing, surveys, behavioral analysis, experimentation, and conversion optimization. Defines research and web measurement strategies, analyzes qualitative and quantitative data, builds dashboards and executive presentations, and translates findings into compelling data stories. Partners with marketing, technology, design, product, and development teams to improve global web experiences, while maintaining scalable research documentation and insight repositories.

Leads business and systems analysis for complex mortgage lifecycle initiatives, translating stakeholder needs into business, functional, data, API, integration, and nonfunctional requirements. Develops process and system models, user stories, acceptance criteria, and testing strategies; supports Agile delivery, releases, defect resolution, and post-production validation. Partners with Product, Engineering, QA, and business teams to manage dependencies, risks, and impacts across web, mobile, legacy, and integrated systems. Mentors Business Analysts and improves analysis standards and practices.

Build and lead Surescripts’ commercial analytics function, including governed Salesforce and CRM data models, KPI definitions, dashboards, forecasting, pipeline analytics, customer journey insights, automated reporting, quota modeling, and territory planning. Manage an analyst and partner with Sales, Marketing, Finance, Data, and Business Applications. Present actionable recommendations to senior leaders while improving data quality and enabling self-service analytics.

Performs business analytics and statistical modeling to identify trends, improve processes, and support decision-making. Develops advanced Excel reports, VBA automations, Power BI dashboards, and data visualizations. Collaborates with business and IT stakeholders, manages shared data in OneDrive and SharePoint, and communicates insights to nontechnical audiences while handling multiple priorities.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.

Leads the data engineering function and manages a team of at least five employees. Owns enterprise data platform execution, scalable pipeline development, Medallion architecture implementation, DataOps, CI/CD, observability, reliability, stakeholder intake, and engineering standards. Partners with data architecture and application teams, mentors engineers, drives delivery, and develops future leaders.