Data & Analytics Engineer

Stellenbeschreibung:

Design, implement, and maintain backend services in Python that power our data access and distribution layer
Build and operate automated build, test, and deployment pipelines following CI/CD and GitOps practices, running on AWS and Kubernetes
Design and maintain data models and schemas (Avro, SQL) for our data pipelines and services, and publish them to downstream consumers via our Kafka-based distribution platform and schema registry
Build access-layer data models in our analytics environment (e.g., Snowflake) so applications can work with the data directly, including via API
Act as the coordination interface between the Data and Tech divisions, aligning priorities, schemas, and timelines across upstream and downstream teams

Requirements

3+ years of experience in analytics engineering, data engineering, or a related role
Strong SQL skills and solid experience in data modeling (e.g., dimensional modeling, star schemas)
Hands-on experience with event streaming and message distribution, ideally Kafka, including schema management with the Kafka Schema Registry (Avro, Protobuf, or JSON Schema)
Experience designing data contracts and schemas between upstream producers and downstream consumers
Experience with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and modern data stack tools (e.g., dbt, CI/CD)
Openness to AI-assisted development workflows (e.g., Claude Code or similar)
Comfort working at the boundary of streaming/operational data and analytical data models, and building access layers that serve applications via API
Strong attention to detail with a quality-focused and structured working style
Interest in data governance, standards, and scalable data architecture
Analytical mindset with strong problem-solving skills
Excellent communication skills in English (German is a plus), with both technical and business stakeholders, combined with a solid understanding of business requirements and contexts

Core Competencies

Demonstrates expertise in backend service design and implementation using Python, with a strong focus on data modeling, analytics engineering, and cloud data warehouse technologies. Proficient in building CI/CD pipelines and managing data schemas for effective data distribution and governance.