Principal Data Engineer, People Data

The Technology, Data, and Intelligence Team

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.

The Principal Data Engineer Opportunity

Okta’s Data Engineering team is on a mission to accelerate Okta’s scale and growth by building platforms that are utilized across the organization. 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 work closely with our People (HR) teams to ensure that valuable candidate and employee data is captured, unlocking insights that will drive Employee experience and growth goals for the entire employee lifecycle from recruiting through employment. This involves leading projects and collaborating closely with data source owners, analytic engineers, data analysts and business users to ensure data availability and integrity.

As a Principal Data Engineer on the Data and Insights team, you will be a key technical leader responsible for architecting, building, and maintaining our most critical and scalable data solutions. This role involves leading complex projects and collaborating closely with product engineers, analytic engineers, data analysts and data scientists to ensure data availability and integrity of the enterprise. As a Principal member of the team you will have a proven ability to tie engineering initiatives to product and business impact. You will also demonstrate a strong sense of judgement, and the ability to influence across the organization.

What you’ll be doing

• Design, build and evolve high scale data solutions that serve as the reference architecture for the organization and simplify data access across the organization
• Develop data quality frameworks, monitoring, anomaly detection, and alerting, with governance, lineage tracking, and change management rigor appropriate for externally reported numbers
• Drive adoption of consistent data modeling patterns, naming conventions, documentation norms, and metric governance standards across the data organization
• Lead cross-functional technical initiatives across data verticals to accelerate delivery and harden data systems
• Navigate ambiguity and make sound technical decisions, balancing short-term delivery with long-term infrastructure investment
• Champion Data Contracts: implement data contracts and schema evolution practices to ensure reliability across global product teams
• Mentor and Scale: Raise the technical bar by mentoring senior engineers, leading cross functional task forces, and fostering a culture of engineering excellence and data driven decision making
• Stay at the forefront of the data landscape, adopting and advocating for emerging technologies and practices to guide the team’s long-term technical direction

What you’ll bring to the role

• Experience: 10+ years in a data engineering role, with proven experience in a senior or lead capacity. Experience with Federal government compliance and security requirements is highly desirable.
• Technical Expertise: Expert-level experience with SQL, ETL/ELT tools (Airflow, dbt), and MPP databases (Snowflake, Redshift). Extensive hands-on experience with AWS services (S3, Lambda, EMR, ECR, EKS).
• People Data expertise: expertise in HR data - specifically in candidate lifecycle, employee records, compensation, and organizational data. Experience designing data models that capture recruiting analytics, engagement signals, and workforce planning metrics.
• Familiarity with Workday data structures is a bonus
• You have experience building and maintaining batch and realtime pipelines using technologies like Spark, Kafka
• Deep experience designing and working with modern lakehouse and warehouse architectures (Databricks, Snowflake) and modern file formats (Iceberg, Delta)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Track record of hands-on collaboration with Data Science, Business, and Product teams

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