Senior Staff Data Engineer - Hybrid/Remote Platform Lead

Ultimate.ai; Austin (TX); Hybrid; USD 200,000 - 300,000; Full time

Job summary

Zendesk seeks a Senior Staff Data Engineer to lead design, development, and scaling of the enterprise data platform. You will work with architects, engineers, analysts, and stakeholders to ensure reliable, observable, secure and governed data services.

The role blends data architecture and engineering execution, influencing technical direction while staying hands-on with data platforms. Hybrid work model with global offices and remote flexibility.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
• 10+ years of data engineering, data architecture, or distributed systems, with at least 5 years in leadership.
• 7+ years of data engineering experience building scalable data platforms.
• 5+ years of experience with cloud columnar databases (Snowflake, BigQuery).
• 3+ years of production experience with dbt and modern ELT pipelines.

Responsibilities

• Lead data engineering projects at scale across multiple teams.
• Plan and deliver complex, multi-team or system projects with external dependencies.
• Own large, mission-critical systems from design to operation.
• Collaborate across teams to research and implement solutions.
• Design end-to-end analytics solutions for customer 360, finance, product, sales.
• Establish engineering best practices and mentor engineers.
• Identify and implement internal process improvements to automate data delivery.

Skills

Data engineering

Data architecture

Distributed systems

Cloud data platforms

dbt

Airflow

Snowflake

AWS

Kubernetes

Terraform

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, or related field

Tools

Airflow

Snowflake

Fivetran

DBT

AWS

GitHub Actions

Docker

Kubernetes

Terraform

Job description

Zendesk seeks a Senior Staff Data Engineer to lead design, development, and scaling of the enterprise data platform. You will work with architects, engineers, analysts, and stakeholders to ensure reliable, observable, secure and governed data services.

The role blends data architecture and engineering execution, influencing technical direction while staying hands-on with data platforms. Hybrid work model with global offices and remote flexibility.