Top Analysis Reporting Jobs in Rochester, NY

Leads sales attribution and incrementality measurement for advertising campaigns using transaction data. Develops causal measurement methodologies, productizes client reporting, advises senior brand and agency clients, sets measurement standards, partners with Product and Engineering on automated pipelines, and grows the measurement team. Requires deep advertising analytics, purchase-based measurement, causal inference, SQL, Python or R, and familiarity with retail media, DSPs, clean rooms, and campaign exposure data.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Analyzes data from multiple sources to identify trends, develop member segmentation and lifecycle models, support competitive analysis, and recommend strategies for improving member participation and financial products. Builds reports, dashboards, and data-driven tools for leaders and staff, collaborates with stakeholders, trains power users, and provides ongoing analytical support using SQL, R or Python, ETL, data modeling, and business intelligence tools.

Own the analytics function supporting fraud, credit risk, and operations. Build automation, reporting infrastructure, fraud-rule backtesting frameworks, risk-label systems, dashboards, and AI-assisted tools. Partner with stakeholders to identify high-impact opportunities, reduce manual work, and track hours saved. This hands-on individual contributor role has managerial scope and includes defining a new analytics function.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

Analyze operational data, build and maintain Power BI dashboards, validate data quality, investigate trends and discrepancies, document reporting definitions, and communicate findings to business stakeholders. Partner with IT, data engineering, and analytics teams to resolve data issues and support reliable reporting. Use SQL, Power Query, DAX, Excel, and AI-enabled analytics tools to transform data and inform process improvements.

Designs scalable ETL/ELT pipelines, analytics-ready data models, and executive dashboards using Python, SQL, Tableau, Power BI, and cloud tools. Performs data mining, modeling, validation, documentation, and reporting while collaborating with technical and business stakeholders. Provides mentorship and technical guidance on analytics engineering, workflow automation, and data best practices. The role is fully remote, requires U.S. citizenship, and requires or permits obtaining Public Trust and Top Secret eligibility.

Supports energy and sustainability analytics for data center clients by cleaning, validating, and analyzing datasets; developing Power BI or Tableau dashboards, dataflows, and reports; documenting methodologies; performing quality checks; and delivering accurate outputs across concurrent projects. Collaborates with energy and sustainability teams, learns internal analytics platforms, and identifies opportunities to improve reporting, automation, and workflows.

Designs and maintains scalable data pipelines, integrations, and foundational data models using Snowflake, dbt, and AWS. Establishes data quality, testing, governance, and documentation standards while supporting analytics, financial systems, and reporting. Partners with Engineering, Analytics, Finance, and Operations, leads technical decisions, improves development workflows, mentors teammates, and incorporates AI coding agents into engineering processes.

Analyzes operational and financial data to identify trends, risks, and opportunities; develops forecasts, regression models, scenarios, dashboards, visualizations, and recommendations for business decision-making. Partners with business leaders to define requirements, communicates insights, and supports data governance, quality, stewardship, modeling, and privacy initiatives.