Overview

Responsible at the expert level for transforming engineering, operational, security, and quality data into actionable insights that improve developer experience and software delivery outcomes. Leads the collection, integration, analysis, and predictive modeling of data across the Software Development Lifecycle (SDLC), observability platforms, incident management systems, production change processes, security tooling, and quality platforms. Contributes to the stability, resilience, quality, and effectiveness of technology delivery through advanced analytics, measurement frameworks, and data-driven recommendations while coaching others.

Primary Responsibilities

• Design and develop analytical frameworks that integrate data across source control, CI/CD pipelines, quality platforms, observability tools, incident management systems, production change systems, security platforms, and software development tools.
• Define, implement, and govern engineering metrics that measure developer experience, software quality, release readiness, operational resilience, engineering productivity, and technology risk.
• Develop predictive models and data-driven insights to identify potential release risks, quality concerns, reliability issues, security vulnerabilities, and delivery bottlenecks before they impact production.
• Perform advanced statistical analysis, trend analysis, and failure analysis to identify patterns impacting software delivery performance and operational health.
• Create engineering scorecards, executive dashboards, and self-service reporting capabilities that provide visibility into engineering effectiveness and delivery outcomes.
• Analyze relationships between code changes, deployment activity, incidents, vulnerabilities, observability signals, and production outcomes to identify leading indicators of risk.
• Partner with Software Engineering, Site Reliability Engineering, Cybersecurity, Infrastructure, Risk, Architecture, and Product teams to establish common measurement frameworks and data definitions.
• Establish standards and best practices for engineering data collection, metric governance, and analytics platform design.
• Provide recommendations that improve software quality, developer productivity, deployment success rates, operational resilience, and customer outcomes.
• Translate complex data and technical findings into actionable recommendations for engineering leaders, executives, and business stakeholders.
• Present findings and insights at leadership reviews, architecture forums, engineering community events, and internal knowledge-sharing sessions.
• Contribute to the evolution of Developer Experience strategy through measurement, experimentation, benchmarking, and continuous improvement initiatives.
• Understand and adhere to the Company’s risk and regulatory standards, policies, and controls in accordance with the Company’s Risk Appetite.
• Identify risk-related issues requiring escalation to management.
• Promote an environment that supports a culture of belonging and reflects the M&T Bank brand.
• Maintain M&T internal control standards, including timely implementation of audit findings and regulatory requirements.
• Complete other related duties as assigned.

Scope of Responsibilities

Serves as a senior individual contributor and subject matter expert for engineering analytics, developer experience measurement, and predictive engineering intelligence.

Supervisory/Managerial Responsibilities

No supervisory responsibilities.

Education and Experience Required

• Associate’s degree and a minimum of 7 years’ systems analysis and/or application development work experience or Bachelor's degree and a minimum of 5 years' systems analysis and/or application development work experience. In lieu of a degree, a combined minimum of 9 year’s education and/or relevant work experience, including a minimum of 5 years’ system analysis and/or application development work experience.
• Advanced experience with data analysis, statistical modeling, predictive analytics, and dashboard development.
• Experience integrating data from multiple enterprise platforms and engineering systems.
• Proficiency with SQL and at least one analytical programming language such as Python or R.
• Experience working with large-scale operational, engineering, or observability datasets.

Education and Experience Preferred

• Experience measuring Developer Experience, Engineering Effectiveness, DORA metrics, Flow Metrics, SPACE Framework, or related engineering productivity models.
• Experience with observability platforms such as Datadog, Splunk, Dynatrace, New Relic, Grafana, or Elastic.
• Experience integrating data from GitLab, GitHub, Jenkins, SonarQube, Jira, ServiceNow, CI/CD platforms, security scanning tools, and cloud environments.
• Knowledge of software development, release management, Site Reliability Engineering, and production operations.
• Advanced statistical analysis and machine learning experience.
• Experience developing predictive risk models and operational analytics.
• Strong storytelling and executive communication skills.
• Experience influencing strategy and decision-making through data.
• Experience leading enterprise analytics initiatives without direct people management responsibilities.

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