Top Analytics Jobs in Hartford, CT

The Analytics Architect will design and implement cloud-based analytics solutions, maintain data pipelines, ensure data quality, and collaborate with teams to enhance operational efficiency.

Design and maintain scalable dbt data models and Looker dashboards; partner with Finance, Product, and GTM to translate requirements into analytics solutions; ensure data quality with Data Engineering; perform analyses, forecasting, and QA to deliver trusted reporting and drive analytics initiatives.

Build and maintain dbt data models, design BI dashboards and reports (Count and other tools), and deliver merchant/issuer analytics. Partner with GTM and product teams to translate data into actionable insights, improve data quality, and lead technical investigations to scale analytics infrastructure.

Lead full-stack analytics at Air by designing and maintaining dbt models and BigQuery transformation layer, ensuring data quality and observability, owning ingestion/orchestration, building self-serve BI (Mode/Hex) and AI-native interfaces, and partnering with product, growth, and GTM teams to deliver trusted data products and strategic insights.

The Enterprise Performance Analytics Engineer builds and maintains data pipelines and models, ensuring data quality and efficient reporting for analytics teams.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

As an Analytics Engineer at Axios, you'll design and maintain data products and visualizations, improving decision-making through actionable insights.

The Senior Data Scientist will lead revenue-driving initiatives, optimize strategies, enhance data analysis techniques, and improve data pipelines for maximum revenue impact.

Lead and scale the Data & Analytics Practice, drive AI-enabled data solutions, oversee client engagements, and mentor a high-performing team. Define growth strategies and modern data architectures while ensuring alignment with business outcomes and collaboration among stakeholders.

Lead measurement strategy for paid and performance marketing, design and interpret A/B and lift tests, run incrementality and attribution analyses, and optimize channel budgets. Build business cases, develop dashboards and BI workflows, evaluate AI-enabled analytics tooling, and enable self-serve analytics. Partner cross-functionally and present insights to leadership to drive experimentation, growth, and LTV outcomes.

Lead development of scalable marketing data models, pipelines, and BI solutions. Partner with Marketing to define metrics, build dashboards, enable self-service analytics, and drive data governance and best practices.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Lead end-to-end funnel analytics and optimization for SMB lending, identifying friction and bottlenecks, designing measurement frameworks, running experiments (A/B testing), and partnering with Risk, Operations, Product, Engineering, and AI teams to deploy automation and AI solutions that improve conversion, cycle time, and customer outcomes while managing risk.

Implement and manage Adobe Analytics across web and mobile, define custom tracking, collaborate with dev and stakeholders, create reports and dashboards, support A/B testing and optimization, deliver insights and training to improve digital performance.

Lead Adobe Analytics architecture and MarTech ecosystem design, implement data layers and tagging across web and mobile, ensure QA and privacy compliance, enable reporting and audience activation, and drive cross-functional measurement and governance for personalization and optimization.

Design and implement a scalable Elasticsearch-based analytics platform integrated with an Oracle data warehouse. Architect data models, ingestion/CDC pipelines, index/shard strategies, and monitoring. Optimize cluster, queries, and caching for sub-second dashboard performance, perform load testing and capacity planning, and produce operational documentation and SLAs.

Design, build, and maintain scalable, documented data models and the Gold data layer using dbt/Coalesce. Ensure data quality through automated tests, observability, version control, and CI/CD. Optimize SQL and warehouse performance, collaborate with Data Engineering and Analytics, and support semantic layers and BI catalogs to enable trusted, self-service insights for Product, Finance, and GTM teams.

The Senior Director will establish and lead the Real-World Data & Analytics group, oversee RWD strategy, engage in complex analytics projects, and build a high-performing team in the oncology domain.

Lead Tableau data visualization strategy: design and optimize high-performance dashboards, establish governance and best practices, mentor teams, enable adoption, and partner with data teams for robust models and performance.

Lead architecture and implementation of a large-scale analytics stack: design foundational pipelines, establish modeling/orchestration standards, integrate AI-assisted engineering tools, translate business questions into version-controlled data models, partner with stakeholders to define metrics and data contracts, migrate logic into maintainable pipelines, and optimize cloud warehouse performance for scale.

Lead the RevOps analytics function: manage an analytics team, own revenue reporting and forecasting, build measurement frameworks, enforce data governance, partner with BI/engineering, analyze funnel and retention metrics, and embed AI/automation to improve revenue predictability and GTM performance.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead the Payments Insights Analytics team, utilizing data and AI/ML for strategy execution, operational excellence, and stakeholder communication.