Data Engineer II, AI Agents

Zendesk

full-time

Posted on: 9/16/2025

Origin: • 🇩🇪 Germany

✨ AI Apply

Mid-LevelSenior

AirflowApacheAWSBigQueryCloudDistributed SystemsETLGoogle Cloud PlatformKafkaKubernetesMongoDBNoSQLPythonSparkSQLTerraformTypeScript

About the role

Design and build data pipelines, services, and infrastructure powering AI-driven insights.
Work at the intersection of product engineering, analytics, and AI to create robust, reliable, and scalable data systems.
Collaborate closely with data scientists, analysts, backend and frontend engineers, and product managers to design data models, define integration patterns, and optimize data workflows.
Support real-time and historical insights for users and enable intelligent, customer-facing features.
Deliver clean, scalable, and reliable data solutions; write well-tested, well-documented code; improve performance and reliability of data systems.
Participate in architecture discussions and help define data standards, schemas, and contracts.
Contribute to planning, reviews, team goals, and knowledge sharing.

3+ years of experience designing and implementing data pipelines and systems in a production environment.
Proficiency with SQL, DBT and at least one general-purpose programming language such as Python.
Experience with batch and stream processing frameworks (e.g., Apache Flink, Apache Spark, Apache Beam, or equivalent).
Experience with orchestration tools (e.g., Apache Airflow)
Familiarity with event-driven data architectures and messaging systems like Pub/Sub, Kafka, or similar.
Strong understanding of data modeling and database design, both relational and NoSQL.
Experience building and maintaining ETL/ELT workflows that are scalable, testable, and observable.
Product mindset — care about the quality, usability, and impact of the data.
Strong communication and collaboration skills.
Curiosity, humility, and a drive for continuous learning.
A Big Plus: experience with cloud-based data platforms (GCP or AWS preferred); familiarity with Looker or other analytics/BI tools; experience with feature stores or ML workflows; understanding CI/CD and infrastructure-as-code like Terraform; comfortable with large-scale distributed systems and production debugging.