Top Analysis Reporting Jobs in Rochester, NY

Build and maintain enterprise data pipelines and infrastructure supporting natural-language querying, semantic search, knowledge graphs, and GraphRAG for the SEC. Responsibilities include onboarding data sources, processing XBRL and financial filing data, managing metadata and lineage, validating data quality, monitoring pipeline health, resolving integration issues, and using AWS data services. The role also requires compliance with federal security standards and collaboration with the Data Architect.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Conducts public health data validation, management, quality assurance, geospatial and molecular HIV analysis using SAS and R. Responsibilities include developing codebooks and data management plans, identifying discrepancies, performing duplicate and comparison procedures, generating reports, interpreting results, disseminating data, and collaborating with analysts, federal partners, jurisdictions, and community-based organizations.

Build, test, maintain, and enhance Workday financial reports, dashboards, and reporting solutions. Gather requirements, validate and reconcile data, document report logic, troubleshoot performance issues, support Workday implementations and releases, and train end users. Partner with Finance, HR, Supply Chain, and other stakeholders to ensure accurate, compliant, and actionable reporting across a nonprofit healthcare system.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake. Architect data foundations to power AI and LLM-enabled workflows, operationalize core SaaS metrics, and build reusable data assets for analytics and activation. Partner cross-functionally to embed analytics into workflows and ensure scalable, secure architecture.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.

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.

Designs and maintains BI solutions, writes optimized SQL and stored procedures, and builds AI-assisted reports and dashboards using Power BI, Tableau, and Excel. Translates business questions into actionable insights for Sales and cross-functional teams, develops data models, supports reporting migrations, and ensures data quality, governance, documentation, and consistency across platforms.

Design and optimize enterprise data architectures and pipelines using SQL, Spark/PySpark, Python, Apache Iceberg, AWS, ClickHouse, and PostgreSQL. Build greenfield application and data solutions, optimize databases and Spark jobs, integrate data virtualization and governance tools, and support CI/CD and infrastructure-as-code practices. Experience with financial services workloads is valued, while telecommunications knowledge is preferred but not required.