Top Senior 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.

Lead and execute analytics implementations (primarily Google Analytics/GTM or equivalents), consult on technical solutions, manage client interactions, QA project work in Asana, mentor junior consultants, and deliver insights and data visualizations for clients.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Analyze sales, marketing, and customer success performance, including pipeline, funnel conversion, campaigns, retention, and revenue drivers. Build and maintain dbt models and marts using Salesforce and modern data-stack tools, while owning data quality and documentation. Contribute to Lighthouse, a BI-as-code platform, and develop AI-assisted analytics workflows. Partner with stakeholders to frame ambiguous questions, deliver recommendations, and communicate progress in two-week sprints.

Lead global Customer Success analytics strategy, architect data and health/adoption models, oversee data pipelines, deliver executive-level storytelling, mentor an analytics team, and drive cross-functional KPI alignment and self-serve BI adoption.

Lead a team of analysts and data scientists embedded in sales to build rigorous pilot analyses, create executive-ready business cases, expand consultative analytics across the partner lifecycle, and publish industry insights while coaching and scaling the Solutions Analytics function.

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

Senior analytics leader who defines analytic strategy, leads a team of SQL analysts and BI developers, architects scalable frameworks for medical economics, risk, population health and value-based care, ensures methodological rigor in DBT/Spark and Quicksight dashboards, drives automation and AI integration, presents to executive stakeholders, and mentors senior analytic talent.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

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 pricing analytics for merchant/platform deals and international market expansion. Define pricing strategy, run economic analyses, design and test pricing constructs, and collaborate with product, partnerships, credit, finance, capital markets, and engineering to optimize portfolio performance.

Lead Databricks-based enterprise data and analytics modernization programs. Define future-state architecture, drive platform modernization, ensure security, scalability, cost optimization, and mentor teams while delivering AI-ready data platforms and governance.

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 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.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Build and scale the analytics foundation: own dbt projects and Snowflake warehouse, create trusted datasets and semantic layers for AI, build ELT pipelines from APIs, implement testing/observability and CI/CD, design reconciliation models, enable stakeholder self-service, and partner cross-functionally to drive data-driven decisions across GTM, Product, Finance, People, and Operations.

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 strategic revenue cycle analyses to diagnose patient billing and collections performance, quantify ROI and value, build executive-ready recommendations and playbooks, partner cross-functionally, and mentor teammates to drive scalable, data-driven improvements.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

Serve as a strategic analytics advisor designing and implementing Adobe Analytics and CJA solutions, conducting measurement workshops and audits, building dashboards, translating insights into action, training clients, and ensuring data security and privacy compliance.

Lead and grow the Revenue Analytics team to deliver strategic sales and customer lifecycle insights. Build forecasting, pipeline, capacity, predictive models, and customer lifecycle analytics to improve revenue, retention, adoption, and expansion. Partner with GTM, Finance, Product, and Enterprise Data to create executive reporting, data governance, AI-enabled self-serve datasets, and integrated analytics that drive business outcomes and support a shift to consumption-based models.

Lead development of scalable, well-tested data products for Revenue: design dbt models, metrics, dashboards, governance, and AI-enable analytics. Drive technical direction, automation, quality, and mentor cross-functional partners to deliver trusted reporting and self-service analytics.

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

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.