Senior Data Engineer – Data Analytics Platform r-19553

Rigby AG; Zürich; Presencial; CHF 140.000 - 190.000; Jornada completa

Descripción de la vacante

Rigby AG in Zürich, Switzerland seeks an experienced senior data engineer to design, build, and operate real-time data pipelines and backend services for decisioning and reporting. You will model data, ensure governance, and deliver secure, scalable solutions using Python/Scala, SQL, and cloud-native technologies, including Databricks, Azure, Kafka, and Kubernetes.

Join a team focused on data quality, security, and robust pipelines across structured and unstructured data, leveraging modern data

Formación

• Bachelor’s or Master’s degree in Computer Science, Data Analytics, Information Systems, or a related technical field; or equivalent training and professional experience.
• 5+ years of experience in ETL/ELT, data warehousing, BI, and integrating data processing/workflow tools into pipeline design.
• 5+ years building and maintaining end-to-end data systems using Python, Scala, or similar languages.
• Strong SQL skills for data analysis and root-cause investigation.
• Experience with Azure cloud data technologies in CI/CD environments.
• Experience with Databricks, PySpark, Spark Streaming, and Delta Lake.
• Hands-on with structured, semi-structured, and unstructured data across RDBMS, data warehouses, in-memory caches, and document stores.
• Familiarity with Azure Data Lake and Blob Storage.
• Solid data modelling knowledge (Data Vault a plus) and strong design/testing skills.
• Experience with event-driven architectures (Kafka, Event Hubs, Flink) and containerised microservices (Kubernetes, Docker, Helm Charts).
• Practical use of dbt and BI tools; strong communication and technical leadership.

Responsabilidades

• Design, develop, and maintain data pipelines and backend services for real-time decisioning, reporting, data collecting, and related functions.
• Data Requirements and Modeling; Data Management and Transformation.
• Produce high-quality, well-tested, and secure code.
• Develop and maintain software to improve data governance and security.
• Develop processes to ensure Data Security and Data Quality.

Conocimientos

ETL/ELT

Data Warehousing

Business Intelligence

Python

Scala

SQL

Azure Cloud

CI/CD

Data Governance

Data Modelling

Data Vault

Event-driven Architectures

Kafka

Azure Data Lake

Databricks

PySpark

Spark Streaming

Delta Lake

Kubernetes

Docker

dbt

BI Tools

Educación

Bachelor’s or Master’s degree in Computer Science, Data Analytics, Information Systems, or a related technical field; or equivalent training and professional experience

Herramientas

Databricks

PySpark

Spark Streaming

Delta Lake

Azure

CI/CD Tools

Kafka

Event Hubs

Apache Flink

Kubernetes

Docker

Helm Charts

dbt

BI Tools

Descripción del empleo

• Design, develop, and maintain data pipelines and backend services for real-time decisioning, reporting, data collecting, and related functions
• Data Requirements and Modeling
• Data Management and Transformation
• Produce high-quality, well-tested, and secure code
• Develop and maintain software designed to improve data governance and security
• Develop processes designed to ensure Data Security and Data Quality

Must-have criteria

• Bachelor’s or Master’s degree in Computer Science, Data Analytics, Information Systems, or a related technical field; or equivalent training and professional experience
• 5+ years of experience in ETL/ELT, data warehousing, Business Intelligence, and integrating data processing/workflow management tools into pipeline design
• 5+ years building and maintaining end-to-end data systems using Python, Scala, or similar programming languages
• Solid experience in utilising SQL for data analysis, investigating data issues, diagnosing root causes, and designing effective solutions
• Experience in working with cloud-based data technologies, preferably on Microsoft Azure, within continuous integration and delivery (CI/CD) environments
• Demonstrated experience working with large-scale datasets using Databricks, PySpark, Spark Streaming, and Delta Lake
• Hands-on experience with structured, semi-structured, and unstructured data across various storage systems, including relational databases (RDBMS), data warehouses, in-memory caches, and document databases
• Familiarity with cloud storage solutions such as Azure Data Lake and Blob Storage
• Solid understanding of data modelling principles (experience with Data Vault is a plus) and strong skills in system design, implementation, and testing
• Experience with event-driven architectures (e.g., Kafka, Event Hubs, Apache Flink) and containerised microservices platforms (e.g., Kubernetes, Docker, Helm Charts)
• Knowledge and practical use of dbt (data build tool), experience with BI tools
• Excellent communication and collaboration skills, ability to provide technical leadership to other developers