Public summary
Seeking an experienced Senior Data & AI Architect in Germany to design, build, and manage scalable data infrastructures for AI applications. The role involves strategic data architecture in regulated sectors such as Financial Services and Med-Tech, ensuring compliance with regulations like the EU AI Act and GDPR. Candidates should have expertise in modern cloud platforms, data modeling, MLOps, and containerization. Responsibilities include client engagement, mentoring, and building robust, compliant data systems. Offers a permanent position with international exposure, mentorship, and career development opportunities in a supportive and inclusive environment.
Location and work setup
- Location
- Berlin, Hamburg
- Remote status
- On-site
- German requirement signal
- No German Required Detected
- Detected job language
- English
Responsibilities
Design and build end-to-end data infrastructure supporting AI applications; develop technical foundations for generative AI and machine learning workloads including feature stores and vector databases; define modeling standards for diverse data types; conduct client data landscape assessments and establish assessment frameworks; integrate regulatory requirements as technical features within CI/CD pipelines; promote clean architecture, automated testing, documentation, and reproducibility; act as liaison between technical and business stakeholders; mentor and coach client teams and junior colleagues.
Qualifications
Minimum 5 years in data engineering, data architecture, or related fields; experience or strong interest in regulated sectors (Financial Services, Med-Tech) and associated regulatory knowledge; proficiency with cloud data platforms such as Databricks, Snowflake, BigQuery; advanced skills in Python, SQL, and orchestration tools like dbt, Apache Spark, Airflow, Prefect; hands-on experience with architectures for Large Language Models and vector databases; familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI; expertise in automated data quality and governance; knowledge of decentralized data architectures (Data Mesh, Data Fabric); experience with CI/CD, Docker, Kubernetes, Terraform; strong consultative communication and mentoring abilities.