Top Senior Analysis Reporting Jobs in Rochester, NY

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Administers and secures Oracle databases supporting DISA contract systems. Responsibilities include database installation, tuning, access management, outage response, disaster recovery, security patching, STIG compliance, scripting, release support, data modeling, legacy data migration, reconciliation, and system integration. The role also supports Appian data stores and coordinates database changes with DISA, hosting providers, ISSOs, and development teams.

Build and maintain SQL-based marketing analytics infrastructure, data models, dashboards, and reporting. Analyze marketing channels, affiliates, conversion funnels, cohorts, revenue, profitability, and partner performance. Translate complex findings into actionable recommendations for budget allocation and optimization. Ensure data quality, governance, documentation, and metric consistency while partnering cross-functionally with Marketing, Sales, Finance, Operations, Product, and Technology. The role is a hands-on individual contributor with potential responsibility for mentoring or managing one analyst.

Develops scalable data models, dashboards, reports, and analytical solutions that support business decisions. Partners with stakeholders to translate requirements, ensures data accuracy and usability, provides technical guidance to end users, documents architectures and processes, and communicates findings to technical and non-technical audiences. The role requires advanced Spotfire, SQL, and business intelligence experience, plus familiarity with modern data platforms and AI-assisted development tools.

Leads a client’s migration from SQL Server, SSIS, and SSRS to Snowflake and Sigma while maintaining legacy reporting operations. Designs data models, ETL pipelines, reports, dashboards, source-to-target mappings, and migration plans; validates reporting parity and coordinates phased cutovers. The role also provides client-facing technical leadership, troubleshooting, documentation, peer reviews, and ongoing support for banking data and reporting systems.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Own regulatory analytics and reporting by building audit-ready datasets, dashboards, data models, pipelines, governance controls, and compliance automations. Analyze complex financial and compliance data, support audits and risk assessments, monitor data quality, and translate regulatory requirements into reliable reporting solutions using Python, SQL, dbt, and Airflow. Partner with engineering, analysts, compliance teams, and leadership to deliver actionable insights and improve regulatory processes.

Lead the architecture, development, and optimization of Snowflake and enterprise data platforms. Design scalable ETL/ELT pipelines, dimensional data models, and medallion architectures while establishing engineering standards for testing, deployment, monitoring, security, and data quality. Partner with stakeholders on technical roadmaps, mentor engineers and analysts, conduct design and code reviews, modernize legacy pipelines, and deliver reliable, reusable data solutions.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.