Top Remote Data & Analytics Jobs in Rochester, NY

Maintains accurate CRM data across HubSpot and ServiceNow by processing data requests, entering and cleaning records, resolving duplicates, managing account relationships, supporting bulk uploads, and addressing cross-system data issues. Partners with Sales, Marketing, and IT Development to ensure accurate integrations, workflows, deal data, and adherence to data processes.

Analyzes business performance data and delivers actionable insights for stakeholders and senior leaders. Develops dashboards, scorecards, KPI reports, and recurring brand reporting while ensuring data accuracy and reliability. Partners with sales, marketing, product, and technical teams to translate business needs into analytics solutions. Performs ad hoc analysis, identifies trends and risks, supports executive presentations, and communicates recommendations clearly.

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

Leads Marketing's data architecture, metric governance, analytical modeling, predictive modeling, experimentation, and executive performance reporting. Manages three to four analysts while remaining hands-on with advanced SQL, Snowflake data validation, dimensional modeling, cross-system reconciliation, attribution, customer analytics, and AI-assisted natural-language analytics. Partners with engineering, governance, product, and marketing teams to define data products, ensure analytical accuracy, and establish scalable measurement standards.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.

Leads the full digital experience research lifecycle, including user interviews, testing, surveys, behavioral analysis, experimentation, and conversion optimization. Defines research and web measurement strategies, analyzes qualitative and quantitative data, builds dashboards and executive presentations, and translates findings into compelling data stories. Partners with marketing, technology, design, product, and development teams to improve global web experiences, while maintaining scalable research documentation and insight repositories.

Leads business and systems analysis for complex mortgage lifecycle initiatives, translating stakeholder needs into business, functional, data, API, integration, and nonfunctional requirements. Develops process and system models, user stories, acceptance criteria, and testing strategies; supports Agile delivery, releases, defect resolution, and post-production validation. Partners with Product, Engineering, QA, and business teams to manage dependencies, risks, and impacts across web, mobile, legacy, and integrated systems. Mentors Business Analysts and improves analysis standards and practices.

Performs business analytics and statistical modeling to identify trends, improve processes, and support decision-making. Develops advanced Excel reports, VBA automations, Power BI dashboards, and data visualizations. Collaborates with business and IT stakeholders, manages shared data in OneDrive and SharePoint, and communicates insights to nontechnical audiences while handling multiple priorities.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.

Leads the data engineering function and manages a team of at least five employees. Owns enterprise data platform execution, scalable pipeline development, Medallion architecture implementation, DataOps, CI/CD, observability, reliability, stakeholder intake, and engineering standards. Partners with data architecture and application teams, mentors engineers, drives delivery, and develops future leaders.