Top Remote Analytics Jobs in Rochester, NY

The Director, Research - Power Markets is responsible for leading the QA/QC process for long-term forecasting, advising leadership, and ensuring market insights are effectively integrated into forecasts.

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.

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.

The Staff Analyst will design analytical frameworks, lead business case development, and provide insights across departments while mastering insurance analytics.

The Analytics Lead will leverage data analytics to optimize credit strategies, monitor portfolio performance, and collaborate on credit policy with stakeholders across technical and non-technical teams.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.

Lead Retail GTM analytics and territory operations: build reporting, automate workflows, design territory models, manage data quality/enrichment, and deliver decision-support tools across Salesforce, Snowflake, and Sigma to scale sales coverage and drive growth.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Lead Finance Data & Analytics to build trusted data sources, governance, and analytics for Finance and cross-functional stakeholders. Manage analysts, drive data strategy and Snowflake data models, produce insights via SQL and Tableau, and deploy AI solutions to optimize finance decision-making and operations.

Design and build Toast’s enterprise analytics layer and AI-ready semantic layer in the data warehouse. Collaborate with domain teams and data engineering to implement hub-and-spoke architecture, define complex business logic in SQL, support scalability, partner with stakeholders, and provide technical mentorship.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

The Data & Analytics team at Surescripts transforms healthcare data into actionable insights, collaborating with various stakeholders to support critical decisions across the healthcare ecosystem.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Lead retail analytics to identify growth and efficiency opportunities, build strategic recommendations (store footprint, pricing, omnichannel), conduct market/competitive research, drive cross-functional initiatives, and deliver executive-ready analyses and frameworks to inform retail and digital strategy.

Lead and scale a casino analytics team to drive data-informed strategy, experimentation, and reporting. Partner with product, data science, and operations to deliver executive insights, build analytical frameworks, and develop high-performing analysts to unlock growth and operational efficiency.

Lead building a data- and AI-enabled growth engine to improve pipeline quality and visibility. Define data governance, segmentation, scoring, and KPIs; operationalize demand-generation campaigns and tooling; prototype AI-driven automation and reporting; partner with vendors and sales/marketing to measure and improve pipeline conversion and ROI.

Ensure accuracy, integrity, and compliance of master and reference data across enterprise systems. Enforce governance, validate and remediate Salesforce and other system records, perform audits/cleansing/de-duplication, support migrations and integrations, produce reports and insights, and partner with global teams to improve data processes and adoption of standards.

This role drives data-driven decision-making in product analytics, collaborating with cross-functional teams to generate actionable insights and evaluate new launches to enhance platform growth.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.

Lead CX analytics strategy and operations by standardizing metrics, enabling self-service analytics, and embedding AI in the analytics lifecycle to enhance decision-making processes.

Use advanced analytics and ML to design and monitor credit risk models, optimize underwriting and credit strategies, partner with Engineering/Product/Compliance, oversee portfolio performance and regulatory alignment, and lead cross-functional initiatives to balance growth, compliance, and risk mitigation.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.