Data Tool Stack Engineer

ZEISS Group

. Design, implement, and maintain the on-premises data tool stack focused on Kubernetes-based environments .

Posted 10/22/2026full-timeBudapest • HungaryMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining Kubernetes-based data tool stacks, optimizing ETL/ELT pipelines, and implementing CI/CD processes. Proficient in leveraging cloud technologies and distributed query engines to enhance data processing and automation.

Highest-signal resume keywords

KubernetesPython ProgrammingETL/ELT ProcessingCI/CD Pipeline ImplementationData Ingestion Solutions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPySparkApache AirflowData Transformation TechnologiesMessaging TechnologiesChange Data CaptureDistributed SQL EnginesObservability TechnologiesNetworking ConceptsInfrastructure-as-Code

Tools & Technologies

Microsoft AzureGrafanaElasticsearchOpenSearchTerraformNginxKafkaMQTTKafka ConnectDebezium

Industry Keywords

Hybrid Cloud IntegrationsDigital TransformationData-Driven Decision-MakingAgentic EngineeringCloud Architectures

Tech Stack

Tools & technologies

AirflowApacheAzureCloudElasticSearchETLGrafanaKafkaKubernetesNGINXPySparkPythonSQLTerraform

About the role

Key responsibilities & impact

Design, implement, and maintain the on-premises data tool stack focused on Kubernetes-based environments
Support hybrid cloud integrations such as Microsoft Azure
Build and operate scalable data ingestion, messaging, and Change Data Capture solutions
Develop, optimize, and orchestrate ETL/ELT pipelines and data processing workflows
Integrate and operate distributed query engines such as Trino/Starburst
Establish and improve CI/CD pipelines for data and platform components
Automate deployment, configuration, and infrastructure management
Design and support networking and connectivity concepts, including Nginx, VNets, and hybrid cloud/on-premises connectivity
Leverage AI agents and agentic engineering approaches in DevOps to improve automation, efficiency, and platform reliability
Implement observability, logging, and search solutions using Grafana, Elasticsearch, or OpenSearch
Contribute to ZEISS's digital transformation and data-driven decision-making

Requirements

What you’ll need

University degree, preferably in computer science, natural sciences, or engineering
Strong software engineering skills with Python
Very good SQL skills
Hands-on experience with Kubernetes and Helm
Practical experience implementing and maintaining CI/CD pipelines
Experience with ETL/ELT processing, data pipelines, PySpark, and workflow orchestration or data transformation technologies such as Apache Airflow, dbt, or comparable solutions
Experience with messaging and streaming technologies such as Kafka, MQTT, Kafka Connect, and Debezium, or comparable change data capture technologies
Knowledge of distributed SQL/query engines such as Trino or Starburst
Knowledge of observability and search technologies such as Grafana, Elasticsearch, or OpenSearch
Good understanding of networking concepts, reverse proxies such as Nginx, network routing, VNets, and hybrid connectivity such as Azure ExpressRoute
Beneficial experience with Microsoft Azure, Databricks, Azure Data Factory, Terraform or other Infrastructure-as-Code technologies, hybrid on-premises/cloud architectures, and AI agents or agentic engineering approaches to DevOps and platform operations

Benefits

Comp & perks

Hybrid work arrangement
Supportive, team-oriented work environment
Opportunity to co-design and develop scalable, high-performance data platforms
Opportunity to work with modern data engineering, DevOps, and AI agent technologies