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About the Role

As a seasoned strategist and engineer, you will design, build, and manage the end-to-end data infrastructure that powers modern AI applications. You will serve as the primary technical partner for clients, acting as a strategic thinker who unleashes complex data ecosystems to create tangible business value. You will prioritize sustainable, scalable, and secure engineering, applying rigor to high-stakes, regulated environments where data quality, compliance, and governance are engineered as core product features.

Responsibilities

Shape the data ecosystems of clients and represent reeeliance’s architectural vision.
Design and build the technical foundation for generative AI and machine learning workloads, including feature stores, vector databases, embedding layers, and scalable ingestion pipelines.
Implement active metadata strategies to design systems that leverage machine-readable metadata for automated data quality, real-time observability, and intelligent pipeline orchestration.
Design and deploy 'embedded governance' frameworks, treating regulatory compliance, data privacy, and security as 'policy-as-code' within CI/CD pipelines.
Define and implement robust modeling standards for structured, semi-structured, and unstructured data using Data Vault, dimensional modeling, and schema-on-read approaches.
Structure and conduct assessments to map a client's data landscape, identify readiness gaps, and establish reusable assessment frameworks.
Treat regulatory requirements as a technical feature, automating policies and security guardrails directly into CI/CD pipelines and platform provisioning.
Champion the 'Golden Path' philosophy, valuing clean architecture, automated testing, documentation, and total reproducibility.
Act as a bilingual bridge, explaining complex technical concepts to business executives and business ROI to developers, listening first to understand client problems.
Coach client teams and mentor junior colleagues, enabling them to adopt modern, high-quality data engineering practices and scaling the architectural mindset.

Requirements

5+ years of experience in data engineering, data architecture, or a related software engineering role.
Experience in or a strong interest in working within regulated sectors like Financial Services or Med-Tech, with an understanding of how regulatory standards shape data architectures.
Deep expertise in architecting and scaling solutions on Databricks, Snowflake, BigQuery, or similar modern cloud platforms.
High proficiency in Python and SQL, solid experience with transformation and orchestration tools (e.g., dbt, Apache Spark, Airflow, Prefect).
Hands-on experience building architectures for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), practical knowledge of vector databases and embedding management.
Solid understanding of MLOps patterns and tooling to ensure models, code, and data versions are fully auditable and reproducible.
Proven ability to build systems focused on automated data quality, data lineage, metadata catalogs, and real-time process monitoring.
Familiarity with decentralized data architectures, such as Data Mesh, and separating platform capabilities from domain data products.
Comfort with CI/CD tools, containerization (Docker, Kubernetes), and IaC (Terraform).
A passion for understanding how tools work under the hood and proactively exploring emerging tech trends.
Strong consultative skills, empathy, and the ability to explain complex technical designs in plain language.
Experience in mentoring team members and helping them grow.
Impeccable verbal and written communication skills in German (C1 level) and English (B2 level).
Strong and discerning listening skills.

Skills

Data Engineering
Data Architecture
Software Engineering
Generative AI
Machine Learning
Feature Stores
Vector Databases
Embedding Layers
Ingestion Pipelines
Active Metadata
Data Quality
Observability
Pipeline Orchestration
Embedded Governance
Regulatory Compliance
Data Privacy
Security
Policy-as-Code
CI/CD
Modeling Standards
Data Vault
Dimensional Modeling
Schema-on-Read
Data Landscape Assessment
Readiness Gap Analysis
Assessment Frameworks
EU AI Act
BCBS 239
GDPR
GxP
Golden Path Philosophy
Clean Architecture
Automated Testing
Documentation
Reproducibility
Git
MLflow
Containerization
Debugging
Workflow Design
Consultative Skills
Empathy
Mentoring
Python
SQL
dbt
Apache Spark
Airflow
Prefect
Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Pinecone
Weaviate
pgvector
MLOps
Kubeflow
SageMaker
Vertex AI
Data Lineage
Metadata Catalogs
Process Monitoring
Decentralized Data Architectures
Data Mesh
Data Fabric
Docker
Kubernetes
Terraform
Agentic Reasoning

Location

On-site

Work Type

Full-time
Permanent

Experience Level

Senior

Benefits

Space to grow: Taking ownership in international projects while continuously expanding your skills
Mentorship and Onboarding: Structured introduction supported by dedicated mentor
Cutting-edge workspace: Work with the latest technology and modern equipment to drive innovative solutions
Long-term stability: A permanent employment in a family-oriented and people-focused company
Diverse & inclusive environment: Join a truly international environment and collaborate daily with teammates from over 15 different nationalities
Balance matters or work-life harmony: A work schedule designed to promote well-being and productivity
Team culture & connection: Social events and structured team-building days held in Berlin and Hamburg
Language courses: Learn, improve or practice your conversational skills on language platform Lingoda
Never stop learning: You’ll receive a dedicated budget to industry-leading platforms like Udemy and MasterClass to sharpen your technical expertise and leadership skills
Job perks (childcare subsidy, company pension scheme, job bike, etc…)

About the Company

reeeliance guides regulated enterprises in turning AI readiness into AI-embedded operations, redesigning the workflows where risk, compliance, and business decisions actually happen.
We design, build, and govern the data foundations that make it possible, combining strategic advisory, data and AI engineering, and deep SAP expertise across Hamburg, Berlin, and Porto.