Senior Director, Engineering and Architecture

Okta•Washington, DC

4h•Hybrid

About The Position

Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. Okta is the leading independent identity provider. The Technology, Data, and Intelligence (TDI) organization is the engine that powers Okta's global workforce, providing the technology and systems that enable our employees to do their best work. Okta is seeking a visionary and results-driven Senior Director, Data Platform and Engineering to lead a global data and analytic engineering team ensuring our data assets are leveraged to their full potential. Reporting to the VP of Data and Insights this role requires a leader who is as comfortable with a technical deep dive as they are with a strategic business discussion. You will be a "player-coach" who can build and mentor a world-class team while personally driving strategic initiatives and ensuring the integrity of our data foundation. A core part of your responsibility will be to champion and enable our AI strategy and technical foundations, ensuring that clean, trusted, and well-governed data is the foundation for all AI initiatives. With the growth of AI in our Products and our business, Okta’s data is more critical to our success than ever. This role will make sure we continue to support the organization run and grow, while building out the platform for the future. Candidates should be energized by that challenge.

Requirements

Experience: 10+ years of progressive experience in data platform, and data engineering, with at least 5+ years in a leadership role.
AI Know How: Demonstrated experience in building data foundations and pipelines specifically to support and accelerate AI and machine learning initiatives.
Technical Expertise: Deep knowledge of modern data stacks, including data warehousing, ETL/ELT pipelines, data modeling, and Tableau. Proficiency with Python, SQL, and experience with cloud-based data environments that enable AI use-cases (e.g., AWS, GCP, Azure).
Governance: Proven ability to build secure solutions for both commercial and public sectors.
Cross-Functional Collaboration: Exceptional ability to translate technical needs across product and IT platforms. Must also have the ability to communicate complex data concepts to both technical and non-technical audiences.
Team Development: A track record of successfully hiring, mentoring, and leading high-performing data teams.
Problem-Solving: A strategic mindset and a passion for solving ambiguous, complex business problems with a rigorous, data-driven approach.

Nice To Haves

Experience with FedRamp and the public sector cloud strongly preferred.

Responsibilities

Lead and Inspire a High-Performing Team: Build, mentor, and scale a diverse, high-performing team of data and analytics engineers, fostering a culture of excellence, collaboration, and continuous learning.
Champion AI Enablement: Act as a critical partner to the AI Engineering teams. Ensure our data and data infrastructure and practices are optimized to support and accelerate AI development and deployment. This includes defining data governance standards, building high-quality training datasets, and developing scalable data pipelines for AI/ML models.
Advance the Data Foundation: Own “back end” data lifecycle for critical business domains, from data ingestion and ETL/ELT pipelines to data modeling, quality assurance, and governance. Ensure a single source of truth for key metrics across the organization.
Enhance Data Security and Governance: Partner with Security leaders to define and implement policies and systems to safeguard data privacy, ensure regulatory compliance, and protect sensitive information. This includes establishing robust data access controls, audit trails, and data retention policies.
Partner Cross-Functionally: Collaborate closely with technology and business partners, including Product and Engineering leadership, to embed data and analytics into both internal and customer facing product development.