Top Senior Analysis Reporting Jobs in Rochester, NY

Owns enterprise data governance activities, including metadata management, data cataloging, data quality rules, lineage, profiling, report certification, PII handling, and governance workflow automation. Partners with data stewards, IT teams, business stakeholders, and senior leaders to improve data integrity, regulatory compliance, reporting quality, and governance processes. The role also supports training, stakeholder workshops, process improvement, and mentoring of less experienced professionals.

Provides data management, database administration, performance reporting, QA/QC, and analytical support for NOAA environmental restoration programs. Develops Power BI dashboards, reports, data models, and visualizations; supports Power Apps, Dataverse, database processes, testing, and user acceptance. Translates program needs into technical requirements, documents business rules and procedures, trains users, troubleshoots data issues, and analyzes performance trends. Requires collaboration with program staff, project managers, leadership, and technical developers, with occasional travel and periodic on-site meetings.

Develop and implement Databricks and AWS data-driven solutions supporting IRS tax-system modernization. Responsibilities include analyzing complex data, prototyping solutions, testing applications, conducting code reviews, debugging and improving software, responding to defects, and potentially leading engineering teams. The role requires collaboration in an Agile environment and communication with technical and client stakeholders.

Designs and implements the data architecture supporting engineering technology, analytics, automation, and AI. Assesses current systems, defines target architecture and governance, develops data models and ETL/ELT pipelines, integrates engineering platforms, and establishes metadata, lineage, stewardship, access, and quality standards. Partners with engineering and Digital Engineering teams to deliver reusable data assets, dashboards, and analytics solutions while coordinating data protection, platform evaluations, roadmaps, and stakeholder communication.

Build and maintain data lakes, data warehouses, and modern data pipelines for a multi-tenant SaaS platform. Design end-to-end data processes, write pipeline and infrastructure code, conduct design reviews, optimize performance, monitor systems, support data governance, and troubleshoot scalability and security issues. Collaborate with technical and business stakeholders, mentor junior engineers, evaluate technologies, document platform architecture, automate testing, and participate in on-call rotation.

Own the architecture, roadmap, and operation of Clarium’s AWS-based data platform. Build reliable batch and near-real-time pipelines, model healthcare supply chain data, optimize Postgres and Snowflake performance, and establish standards for testing, deployment, quality, and governance. Partner cross-functionally to create durable data models, lead multi-quarter initiatives, manage sensitive data controls, and mentor engineers.

Leads and mentors the Data Engineering team while remaining hands-on in designing, building, and maintaining scalable data pipelines, warehouses, integrations, and infrastructure. Establishes architecture, reliability, quality, and performance standards; guides technical delivery; and resolves complex issues. Partners with Finance and business stakeholders to translate data needs into practical solutions, sets technical direction, evaluates technologies, and communicates strategy, risks, and recommendations to leadership.

Leads and delivers complex data engineering projects, designing scalable data infrastructure, pipelines, warehouses, and data quality solutions. Recommends tools and architectures, optimizes data movement and performance, collaborates across technical teams, mentors engineers, and translates technical concepts for clients and nontechnical stakeholders. The role may also involve real-time processing, data modeling, transformation, security, and MLOps.

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.