Senior Cloud Data Engineer

This role is four days onsite at our Seneca One Buffalo, NY location, with the flexibility to work from home one day per week.

The Senior Principal Cloud Data Engineer is a senior‑level individual contributor responsible for defining, building, and evolving enterprise cloud data platform capabilities in Microsoft Azure. This role provides deep technical leadership across cloud data architecture, platform engineering, and enablement, ensuring that enterprise data solutions are scalable, secure, and consistent with M&T’s data and technology strategies. The role partners closely with Enterprise Architecture and the Data organization to help define and drive the enterprise data strategy from a cloud platform perspective.

In addition to influencing strategy, this role is accountable for owning and engineering the associated Azure‑based data platform capabilities that enable analytics, reporting, and data‑driven solutions across the enterprise.

• Enterprise Cloud Data Platform Leadership

  Define and influence the architecture and engineering standards for enterprise cloud data platforms in Azure. Serve as a senior technical authority for cloud data platform decisions, including trade‑offs across scalability, security, performance, and cost. Provide technical leadership for the evolution of shared data capabilities used by analytics, reporting, and application teams.

• Data Strategy Partnership

  Partner closely with Enterprise Architecture and the Data organization to:
  Help shape and refine the enterprise data strategy, translate data strategy into actionable cloud platform capabilities, ensure cloud data platform designs align with enterprise data models, governance standards, and long‑term architectural direction. Act as a technical bridge between data strategy and engineering execution.

• Cloud Data Platform Engineering (Azure)

  Own the design and build‑out of core Azure‑based data platform capabilities, including data ingestion and integration patterns, enterprise data storage and lakehouse architectures, data processing and transformation platforms, analytics and consumption enablement patterns. Establish opinionated, reusable platform components and reference implementations rather than one‑off data solutions. Ensure data platforms are built with automation, resilience, and security by design.

• Standards, Patterns & Reference Architectures

  Develop and maintain approved reference architectures and technical patterns for cloud‑based data solutions. Define standards for data pipelines, storage, partitioning, lifecycle management, access control, encryption, data protection, monitoring, observability, and operational support. Ensure patterns are adoption‑ready and consumable by delivery teams.

• Engineering Enablement

  Reduce delivery friction for data engineers and analytics teams by providing clear technical guidance, reusable templates and frameworks, and platform‑embedded controls and defaults. Promote consistency and reuse across data solutions while enabling appropriate flexibility.

• Hands‑On Technical Contribution

  Remain actively engaged in architecture and design reviews, proofs of concept, platform pilots and early‑stage data initiatives. Validate that cloud data standards and platforms are practical, scalable, and operationally viable. Provide technical mentorship to senior cloud and data engineers.

• Cross‑Functional Collaboration

  Work closely with enterprise and solution architecture, data engineering, analytics and governance teams, cloud and infrastructure engineering, and security and risk management teams. Communicate data platform decisions and standards clearly to engineering leadership and senior technology stakeholders.

Senior individual contributor role with enterprise‑level impact. Direct accountability for the design and build‑out of shared cloud data platform capabilities in Azure. No direct people‑management or budget ownership.

• Adoption of enterprise data platform capabilities
• Reduction in bespoke or inconsistent data solutions
• Improved scalability and reliability of data workloads
• Alignment between data strategy and platform…