Top Senior Data & Analytics Jobs in Buffalo, NY

The Lead Insurance Data Analyst will analyze data in healthcare, focusing on insurance billing processes, consolidate data from multiple sources, and provide actionable insights for department improvements.

The Principal Decision Scientist will lead data-driven insights for strategic decisions, driving business impact through advanced analytics, AI, and mentoring within a data science team.

The Senior Data Engineer will focus on Data Modeling, utilizing SQL, Snowflake, and Apache Airflow to develop efficient data models and deliver data products.

The Associate Director, Commerce Data leads data organization and usage to measure success and enhance business growth, collaborating across teams and ensuring data accuracy and reliability in reporting and forecasting.

Lead the transformation of insurance operations into scalable solutions on the Majesco platform, ensuring alignment with product architecture. Guide clients through the implementation of insurance strategies across policy administration, billing, claims, and digital experiences.

Design, build, and maintain production data pipelines and tooling (CI/CD, testing, validation) using Snowflake, dbt, and orchestration tools. Model, transform, document, and optimize data to produce reliable curated datasets for analytics and solutions, enforce engineering best practices, and mentor teammates while ensuring scalability, observability, and data integrity.

Design and optimize BI semantic models and analytics workflows using PowerBI and Tabular Editor; build and transform large-scale datasets in BigQuery with dbt; implement CI/CD and deployment via GitHub and Azure DevOps; collaborate with stakeholders to deliver actionable visualizations and integrate Agentic AI into analytics pipelines.

Lead a team of Data Scientists and Analysts, developing AI and data-driven solutions, driving analytics insights, and fostering collaboration across teams.

Lead oversight of data management for assigned studies, manage external vendors, review and approve DM documentation, perform secondary UAT of EDC, implement RBQM oversight, and provide cross-functional input on protocols and study deliverables to ensure data quality and regulatory compliance.

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.