Director, Data Engineering & Architecture

EdgeConneX•Herndon, VA

22h•Hybrid

About The Position

Led by an experienced management team and supported by a strong investor group, including large and experienced institutions and strategic partners, EdgeConneX offers a dynamic, fast-paced work environment where we are bringing flexibility, proximity, power, and connectivity to some of the world’s key businesses. With major offices in Herndon, Denver, and Amsterdam, we have a global footprint and a unified team of employees committed to providing a premier customer experience and delivering the full spectrum of data center solutions, from core to edge, like no other data center provider can do. Focused on driving innovation and helping our customers define and deliver their own unique vision for the Edge, at any scale, in any market worldwide, for any requirement, we are building tomorrow’s data center infrastructure, today for some of the world’s most demanding Network, Content, and Cloud customers. The Director of Data Engineering and Architecture will lead and actively contribute to the design, development, and operation of the company’s enterprise data platforms. This role is a hands-on leadership position responsible for data architecture, ETL development, and data pipeline implementation supporting analytics, reporting, and operational use cases across the enterprise. The role partners closely with IT, Sustainability, Finance, Operations, and Business stakeholders to ensure scalable, secure, and high-quality data solutions. This position would be ideally based in our Herndon, VA HQ with the flexibility to collaborate in a hybrid manner where working remotely 1-2 days per week would be the norm. The candidate must be capable of global travel and will likely travel 3-4 weeks per year.

Requirements

Significant professional experience in data engineering and data architecture roles, including leadership or management responsibilities
Experience supporting enterprise‑scale data warehouses and analytics platforms in a complex organization
Working knowledge of Azure cloud services (App Services, Azure SQL, Key Vault, Storage, Functions, etc.). Being able to translate those skills to AWS/GCP is preferable.
Hands-on experience with event queues and real-time analytics
Proficiency with Agile methodologies
Strong leadership and mentoring skills with a collaborative, team‑oriented mindset
Strong communications, both written and verbal, in leading teams and coordinating with peers and other disciplines on driving projects/issues to closure
Commitment to organizational excellence and constant awareness to seek opportunities to improve processes/quality of deliverables
Superb ability to prioritize and manage multiple projects/responsibilities
Superb ability to meet committed timelines, leveraging assistance/input from peers
Commitment to operational excellence, data quality, and continuous improvement. Strong attention to detail.

Nice To Haves

Prior experience in a technical lead or mentoring role would be ideal

Responsibilities

Lead, mentor, and manage a team of data engineers, database administrators, and architects, providing hands‑on technical guidance and architectural oversight.
Design and maintain enterprise data architecture standards, patterns, and best practices across data ingestion, transformation, storage, and consumption layers.
Architect, build, and optimize ETL and ELT pipelines supporting enterprise analytics, reporting, and operational workloads.
Own and evolve the enterprise data warehouse and related data platforms to ensure performance, scalability, reliability, and data quality.
Partner with business and analytics stakeholders to translate requirements into well‑designed, maintainable data solutions.
Establish data engineering development standards, code quality practices, testing, monitoring, and deployment processes.
Ensure secure, compliant handling of enterprise data in alignment with governance, privacy, and security requirements.
Collaborate with infrastructure, application, and analytics teams to integrate data platforms with upstream and downstream systems.
Drive continuous improvement through automation, performance tuning, cost optimization, and adoption of modern data engineering practices.
Contribute directly to complex data engineering initiatives as a hands‑on technical leader when needed. Applying innovation and newer technologies as appropriate.