Top Data & Analytics Jobs in Hartford, CT

Lead data-driven strategies and analytics at PwC, motivating teams to improve business performance and decision-making through advanced techniques and collaboration.

Analyze auction data to identify performance drivers and risks, collaborate with commercial teams, and provide actionable insights to optimize DSP outcomes.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Build and maintain production-grade data models and pipelines for the Compliance Data Mart, implement data quality frameworks and monitoring, partner with engineering to resolve data gaps, support regulatory exams and audits, and automate manual workflows into scalable pipelines and self-serve tooling.

Lead monitoring and optimization of programmatic ad campaigns using real-time and historical data. Diagnose performance issues, design A/B and multivariate experiments, partner with AdOps and Data Science to operationalize ML-driven solutions, and drive automation of campaign decisioning to scale revenue and efficiency.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

As a Staff Product Analyst, you'll define analytics strategies, build measurement infrastructure, design experiments, and partner with product teams to boost self-serve conversion and product adoption.

The Senior Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

Build and maintain dbt models, write SQL to investigate and validate data, implement data quality checks, document lineage and metrics, resolve production issues (including on-call), and partner with analysts and business teams to deliver production-ready data products using modern warehouse tooling and AI-assisted frameworks.

Partner with Customer Success and CS Ops to build post-sales analytics: customer health scoring, renewal and renewal pipeline reporting, billing and support signal analysis, and expansion/upsell dashboards. Own dashboard ecosystem, translate stakeholder requests into scalable reporting, collaborate with Finance and Data Engineering, and leverage AI tools to accelerate analytic workflows and maintain data quality for retention and expansion decision-making.