Description
Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organisations to safely embrace this new era.
The Data Engineering Team Our Data Engineering team is part of the Technology Data & Intelligence organisation, on a mission to accelerate Okta’s scale and growth. We focus on building platforms and capabilities utilised across the organisation by sales, marketing, engineering, finance, product, and operations.
The Senior Data Engineer Opportunity As a Senior Data Engineer, you will be responsible for designing, building, and maintaining scalable solutions. This role involves collaborating with data engineers, analysts, scientists, and other engineers to ensure data availability, integrity, and security.
Responsibilities:
- Design and Develop Scalable Platforms: Build and maintain scalable data platforms using AWS, Snowflake, dbt, and Databricks.
- Infrastructure as Code: Utilise Terraform to manage infrastructure as code, ensuring consistent and reproducible environments.
- CI/CD Management: Develop and maintain CI/CD pipelines for data platform applications using GitHub and GitLab.
- AI/ML Infrastructure: Engineer robust data pipelines that fuel high-performance AI and ML models by delivering reliable, feature-rich datasets.
- Data Governance & Strategy: Implement comprehensive data governance frameworks to ensure data lineage, compliance, and quality across all analytical platforms.
- Security & Compliance: Conduct vulnerability scans, apply necessary patches, and ensure data security meets industry standards.
- Innovation: Explore cutting-edge tools like "data engineer agents" to automate routine tasks and free up time for high-leverage, custom pipelines.
- Cross-Functional Collaboration: Partner with product managers, UX designers, and other developers to deliver end-to-end solutions.
Requirements:
- Education: BS in Computer Science, Engineering, or another quantitative field of study.
- Experience: 5+ years of professional experience in a data engineering or full-stack role.
- SQL & ETL Mastery: Expert-level knowledge of SQL, ETL tools such as Airflow and dbt, and relational/columnar MPP databases like Snowflake or Redshift.
- Cloud Infrastructure: Hands-on experience with AWS (S3, Lambda, EMR, EC2, EKS).
- Modern Data Architecture: Experience with lakehouse architectures (Databricks) and file formats like Iceberg and Delta.
- Containerisation: Proficiency with Docker and Kubernetes for packaging and distributing applications.
- Communication: Excellent oral and written communication skills for both technical and non-technical audiences.