Top Data & Analytics Jobs in Buffalo, NY

Support an insurance actuarial department by cataloging and classifying data, maintaining master data mappings and schemas, auditing data quality, enforcing governance policies, and ensuring regulatory compliance. Collaborate with technical and business stakeholders, conduct Alation and Excel training, and lead data governance projects aligned with organizational objectives.

Lead Business Analyst for Payment Integrity responsible for analyzing client data, capturing business requirements, designing product solutions, supporting bids, documenting processes, driving implementations, and delivering KPI-driven insights. Works cross-functionally with sales, marketing, finance, and delivery to build business cases and improve claims auditing and analytics outcomes.

Lead and manage a remote Data Engineering team to build scalable data pipelines and lakehouse solutions. Own execution of Medallion architecture using Databricks, Boomi/Rivery, dbt, Delta Lake and SQL Server; implement DataOps, CI/CD, observability, and stakeholder intake while growing engineers and improving team maturity.

Manage transportation data integrity from intake through ETL and business intelligence platforms. Review and correct data errors, resolve quality and flow issues, analyze large transportation datasets, and create KPI reporting solutions. Deliver client business reviews covering spend, trends, savings, and market impacts. Serve as a client contact while collaborating with audit, finance, development, and analytics teams to improve reporting tools, software, and customer outcomes.

Produces publishable maps, graphs, and visualizations of conflict patterns and analysis outputs. Transforms complex datasets into compelling visual stories, develops visualization guidelines, researches new methods and software, and supports visual brand development. Collaborates with regional experts, media, partners, and external stakeholders, responds to journalist inquiries, ensures data accuracy, and contributes to outreach, reporting, coordination, and communications activities.

Provide insights and recommendations to improve end-to-end purchase customer journeys across web and app for B2C and B2B. Lead A/B testing and experimentation, ensure accurate data collection and impact measurement, build self-service reporting and visualizations, query and process data, and present findings to stakeholders and senior leadership.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Own high-impact data products, ingestion pipelines, and granular data feeds end to end. Analyze massive datasets to answer client and stakeholder questions, ensure data accuracy, and derive actionable insights. Improve products and pipelines by developing methodologies, scaling third-party datasets, and creating data modules. The role requires clear communication, attention to detail, methodical problem solving, prioritization, experimentation, and strong ownership. Training includes SQL, PySpark, Python fundamentals, and internal tools.

Leads Commercial Markets Operations intelligence, business intelligence, analytics, reporting, dashboards, and strategic information initiatives. Manages analysts, develops performance metrics, oversees operational reporting, supports executive decision-making, and drives process improvement. The role also manages resource planning, budgets, stakeholder alignment, SharePoint stewardship, and adoption of innovative information solutions across Sales Operations and Commercial Markets.

Partner with customers and sales to design and present scalable Databricks Lakehouse architectures, build reference architectures and demos, consult on big data, ETL, and ML projects, validate integrations, and drive technical wins and adoption across accounts.

Design, optimize, and scale Snowflake-based data architecture and pipelines. Implement data observability, operationalize MLOps, build semantic enterprise models, and deploy AI-assisted agents and automation for production analytics and commercial data products. Share cross-functional ownership for pipeline resilience, monitoring, and model lifecycle management.