Top Senior Data & Analytics Jobs in Rochester, NY

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

The Senior Director will manage data partnerships and relationships to enhance Experian's Financial Services business, ensuring compliance and optimizing data ROI.

The Business Intelligence Analyst will analyze pharmaceutical transaction data, develop visual data models, and collaborate cross-functionally to drive insights and improvements in business strategies.

As Associate Director of Data Strategy, you'll develop data strategies for clients, drive data insights and implement technology for media effectiveness, contributing to overall growth and strategy.

As a Senior Analytics Engineer, you'll build data pipelines and dashboards, partner with stakeholders for KPI development, and ensure data quality across the company.

The Director of Data Analytics & Business Intelligence leads analytics functions, building models and dashboards while guiding a team. Responsibilities include analytics infrastructure management, cross-functional collaboration, and driving AI/ML initiatives.

The role involves acting as a subject matter expert in Data and AI, providing technical expertise during sales processes for Azure-based solutions, and mentoring team members to enhance client experiences.

Lead legacy-to-SaaS data migrations: design and execute ETL, extract/transform/load data, validate integrity, map business workflows to target models, engage clients, document processes, support governance, and train stakeholders to ensure accurate, secure conversions.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Lead development of scalable enterprise analytics and data stewardship. Perform advanced data analysis, assess data quality and lineage, define cleansing strategies, manage metadata and business glossary, support enterprise data warehouse and analytic product backlogs, and collaborate with cross-functional teams to enable self-service and advanced analytics.