Sr. Director, Enterprise Data Platform, Integration & Architecture

Overview

We are designing the grid of the future! The Sr. Director, Enterprise Data Platform, Integration & Architecture will lead the strategy, buildout, and operation of EPE’s enterprise data ecosystem and related integration/platform services.

Responsibilities

• Enterprise Data Platform & Architecture
  Lead the design, implementation, and operation of EPE’s enterprise data platform, including modern data lake/lakehouse capabilities.
  Define scalable data architecture patterns that support operational reporting, financial visibility, analytics, AI/ML, and GenAI use cases.
  Establish curated data layers, canonical data models, and reusable data domains across the enterprise.
  Ensure platform architecture aligns with enterprise standards for scalability, security, performance, and long-term maintainability.

• Data Engineering & Integration
  Build and govern reliable data pipelines, integration patterns, and data services across enterprise systems.
  Partner with business applications, analytics, infrastructure, and security teams to ensure data flows are accurate, timely, and resilient.
  Support the integration of enterprise platforms into EPE’s broader data architecture.
  Partner with software and engineering teams to expose specialized engineering systems, modeling/simulation tools, and related data assets through secure, documented, versioned, and observable service interfaces for approved downstream consumers.
  Manage the data and integration backlog, prioritizing work based on business value, risk, and strategic alignment.

• Data Governance, Quality & Stewardship
  Establish and mature EPE’s enterprise data governance framework.
  Define standards for data quality, metadata, lineage, classification, access controls, and observability.
  Partner with business leaders to define data ownership, stewardship responsibilities, and accountability across key data domains.
  Lead governance forums that drive alignment, adoption, and trusted use of enterprise data.

• Responsible Platform Enablement for AI Systems
  Ensure EPE’s data ecosystem is prepared to support AI platforms, GenAI use cases, AI agents, and advanced analytics.
  Partner with business, engineering, software, and AI delivery teams to enable prioritized AI opportunities by delivering the required data products, integration services, access patterns, and platform capabilities.
  Establish responsible data access and AI practices, including governance for sensitive data, model inputs, and ethical use.
  Ensure AI-related data capabilities are secure, scalable, and aligned with enterprise architecture and software development practices.

• Platform Operations, Security & Reliability
  Operate data and integration platforms with a focus on reliability, performance, monitoring, and incident response.
  Provide visibility into platform health, delivery progress, risks, costs, and tradeoffs.
  Ensure data platforms and services meet enterprise standards for access control, encryption, privacy, compliance, and security.
  Partner with Cybersecurity, Legal, HR, and Compliance on incidents involving data misuse, breaches, or policy violations.

• Leadership, Vendor & Business Partnership
  Partner with executives and business leaders to align data initiatives with enterprise and departmental priorities.
  Manage vendor relationships, platform/tooling decisions, contracts, and delivery outcomes.
  Drive adoption, enablement, and training to help teams use enterprise data capabilities effectively.

Qualifications

• Experience Bachelor’s degree in Information Technology, Computer Science, Data, Engineering, or related field; master’s degree preferred.
  15+ years of experience in data, technology platforms, enterprise applications, or IT leadership.
  Proven experience leading enterprise data platform initiatives, such as Snowflake, lakehouse, or cloud data ecosystems; AWS experience preferred.
  Experience building or leading data engineering, governance, integration, or platform teams.
  Experience enabling AI-consuming systems — including GenAI, RAG, analytics, automation, and use cases spanning both IT-governed and software-team-owned applications — through governed data products, APIs, metadata, access controls, observability, and reusable integration patterns.
  Experience designing secure access patterns for internal software teams and approved external technical partners, including least-privilege access, sandbox/prod separation, audit logging, credential management, and data-sharing controls.

• Technical & Business Skills
  Strong understanding of modern data platforms, data lake/lakehouse architecture, and cloud data ecosystems using both commercial off-the-shelf and open-source software.
  Experience with data integration patterns, including ETL/ELT, APIs, streaming, and batch processing.
  Knowledge of data governance, MDM, data quality, metadata, lineage, and data classification practices.
  Demonstrated ability to design and govern contract-first integration patterns, including APIs, event streams, batch interfaces, semantic data products, schema/version management, testing, error handling, documentation, and service-level expectations.
  Experience creating AI-ready data products with clear ownership, authoritative sourcing, semantic definitions, metadata, lineage, quality thresholds, sensitivity classification, permitted-use rules, and machine-readable documentation.
  Experience establishing observability for data and integration services, including freshness, completeness, lineage, latency, failures, usage patterns, access anomalies, cost, and downstream impact.
  Understanding of security, privacy, compliance, and access control requirements for enterprise data platforms.
  Ability to translate business strategy into practical roadmaps, measurable outcomes, and delivery plans.
  Ability to establish practical governance patterns that make approved data and system access faster, safer, and more reusable while mitigating risks.