Senior AI Data Engineer — AI-Driven Data Pipelines

Zendesk, Inc.; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Zendesk is seeking a Senior AI Data Engineer to join the Analytics Prototyping (ZAP) team in London. You will design and maintain ELT pipelines, build SQL data models with dbt, and apply AI-enabled techniques to improve data assets behind Zendesk’s reporting.

You'll collaborate across code, dbt models, and datasets, optimizing performance, scalability, and cost. Strong Python, cloud data warehouses, and communication skills are essential for cross-functional partner success.

Qualifications

• 5+ years of data engineering experience building and maintaining data pipelines in big data environments.
• Extensive experience with SQL for data modeling and analysis.
• Hands-on dbt experience and knowledge of data modeling (Kimball, Inmon).
• Python or similar language for data transformation and automation.
• Experience with cloud columnar databases and multiple DBs.
• Proven performance testing, capacity planning, and cost optimization for large-scale pipelines.
• Excellent communication and collaboration skills.

Responsibilities

• Develop and maintain ELT pipelines for business reporting and analytics.
• Build and optimize SQL-based data models using dbt and ETL tools.
• Support AI-enabled operating model with emerging technologies.
• Identify and implement data delivery and processing improvements.
• Collaborate to translate requirements into scalable data models and pipelines.
• Contribute to the team’s technical vision and data systems.

Skills

Data engineering

SQL

Python

dbt

Cloud DWs

Performance tuning

Communication

Tools

Snowflake

BigQuery

Redshift

Airflow

Kafka

Spark

Job description

Zendesk is seeking a Senior AI Data Engineer to join the Analytics Prototyping (ZAP) team in London. You will design and maintain ELT pipelines, build SQL data models with dbt, and apply AI-enabled techniques to improve data assets behind Zendesk’s reporting.

You'll collaborate across code, dbt models, and datasets, optimizing performance, scalability, and cost. Strong Python, cloud data warehouses, and communication skills are essential for cross-functional partner success.