Senior Data Engineer
Okta · Bengaluru · 5+ yrs experience · Posted 2026-07-21
Tech stack: AWS, Docker, Kubernetes, SQL, Terraform
Apply on the company site · Get a referral for this role
Okta salary & ratings · Okta interview process · More live openings
About the role
Responsibilities:
- Design and Develop Scalable Platforms: Build and maintain scalable data platforms using AWS, Snowflake, dbt, and Databricks.
- Infrastructure as Code: Utilize 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.
Qualifications:
- to the role
- 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.
- Containerization: Proficiency with Docker and Kubernetes for packaging and distributing applications.
- Communication: Excellent oral and written communication skills for both technical and non-technical audiences.
- And extra credit if you have experience in any of the following!
- Experience working on latest AI technologies
- Experience coaching and developing individuals for increased effectiveness
- Familiarity with cloud cost optimization (FinOps)
- Background in SaaS, security, or identity industries
- Hybrid_Bengaluru
- LI_Linkedin P24720_3369507
- Supporting Your Well-Being
- Driving Social Impact
- Developing Talent and Fostering Connection + Community
Qualifications
- to the role
- 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.
- Containerization: Proficiency with Docker and Kubernetes for packaging and distributing applications.
- Communication: Excellent oral and written communication skills for both technical and non-technical audiences.
- And extra credit if you have experience in any of the following!
- Experience working on latest AI technologies
- Experience coaching and developing individuals for increased effectiveness
- Familiarity with cloud cost optimization (FinOps)
- Background in SaaS, security, or identity industries Hybrid_Bengaluru LI_Linkedin P24720_3369507
- Supporting Your Well-Being
- Driving Social Impact
- Developing Talent and Fostering Connection + Community
Responsibilities
- Design and Develop Scalable Platforms:
- Build and maintain scalable data platforms using AWS, Snowflake, dbt, and Databricks.
- Infrastructure as Code:
- Utilize 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.