Top Data & Analytics Jobs in Hartford, CT

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.

Owns the design, development, deployment, and support of B2B data pipelines using BigQuery, dbt, Python, and orchestration frameworks. Translates client requirements into scalable data models, integrates CRM and marketing data, builds measurement and activation pipelines, and ensures quality through testing, validation, monitoring, and observability. The role also standardizes reusable B2B data capabilities, troubleshoots production issues, and supports account-level marketing-to-revenue measurement across multiple clients.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Leads end-to-end data architecture, ETL pipelines, data models, quality frameworks, monitoring, and BI platform operations. Manages Tableau infrastructure, drives project plans and agile sprint delivery, establishes AI-enabled analytics capabilities, and partners with cross-functional stakeholders. Coaches and manages data engineering team members while ensuring data availability, accuracy, security, documentation, and continuous improvement.

Analyze large datasets using SQL and Python, build dashboards and recurring reports, identify trends and data-quality issues, and translate ambiguous business questions into actionable insights. The role partners with product, customer success, and engineering teams, documents metrics and methodologies, and presents findings to technical and non-technical stakeholders. Healthcare data, supply chain analytics, modern analytics engineering, and BI visualization experience are advantageous.

Leads Ford’s multi-touch attribution team and enterprise marketing measurement capability. Responsibilities include managing eight employees, vendor relationships, data pipelines, analytics intake, taxonomy governance, data quality, statistical validation, causal inference, dashboards, and reporting. The role uses GCP, Python, and SQL to automate attribution workflows, evaluates marketing effectiveness, oversees budgets and contracts, partners with media agencies, and communicates insights to technical and business stakeholders. It also promotes AI/ML and generative AI adoption while ensuring privacy, policy compliance, and alignment with Marketing objectives.

Analyze, clean, and validate data from multiple sources; build reports, dashboards, and visualizations; identify trends and opportunities; track KPIs; investigate discrepancies; document analytical processes; and communicate actionable insights to cross-functional business and product stakeholders.

Build and maintain a regulatory reporting data platform using SQL and dbt, including models, automated validations, data pipelines, and testing. Investigate data issues end to end, validate submissions, support audits and legal data requests, monitor upstream changes, collaborate with product, engineering, compliance, and leadership, and participate in on-call coverage. WOOF WOOF

Develop and execute a rare disease data strategy for the U.S. Commercial organization. Translate business priorities into data roadmaps, establish governance and metadata standards, coordinate data sourcing and vendor evaluations, manage delivery risks and dependencies, drive adoption of new ways of working, and report progress to senior stakeholders. Ensure data strategy activities align with privacy policies and regulations while optimizing data assets, products, and lifecycle management.