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DevologyX Linkedin · Posted 6d ago

Data Architect

Germany

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About Role

We are looking for an experienced Data Architect to define and implement modern enterprise data architectures that support analytics, AI, Machine Learning and business intelligence.

The Data Architect will be responsible for designing scalable, secure and governed data platforms across cloud and on-premise environments.

You will work closely with Data Engineering, AI/ML, Cloud, Security, Architecture and Business teams to establish how data is collected, integrated, stored, governed and consumed across the organisation.


Key Responsibilities

  • Define and own enterprise data architecture and data platform strategy.
  • Design scalable architectures supporting analytics, AI and Machine Learning workloads.
  • Develop data models, data flows and integration patterns.
  • Design modern data warehouses, data lakes and Lakehouse architectures.
  • Define architecture for structured, semi-structured and unstructured data.
  • Establish data governance, quality, lineage and metadata strategies.
  • Work with Data Engineering teams to translate architecture into production platforms.
  • Define standards for data ingestion, transformation and consumption.
  • Design batch and real-time/streaming data architectures.
  • Integrate data across multiple enterprise systems and applications.
  • Ensure data architectures comply with security, privacy and regulatory requirements.
  • Work closely with AI teams to ensure data platforms support AI/ML and Generative AI workloads.
  • Evaluate new data technologies and make recommendations to the organisation.
  • Produce architecture documentation, standards and technical roadmaps.
  • Provide technical leadership and guidance to Data Engineers and other technical teams.


Required Technical Skills

Cloud & Data Platforms

Strong experience with one or more:

  • AWS
  • Microsoft Azure
  • Google Cloud Platform


Experience with modern data platforms such as:

  • Databricks.
  • Snowflake.
  • Microsoft Fabric.
  • Azure Synapse.
  • AWS data services.


Data Engineering

  • Apache Spark.
  • PySpark.
  • SQL.
  • ETL/ELT.
  • Data pipelines.
  • Data integration.
  • Batch and real-time processing.
  • API-based integration.


Data Architecture

  • Data Lake / Data Warehouse architecture.
  • Lakehouse architecture.
  • Data modelling.
  • Dimensional modelling.
  • Data Mesh concepts.
  • Event-driven architecture.
  • Microservices and API architectures.
  • Streaming architectures such as Kafka.


Data Governance

  • Data governance frameworks.
  • Data quality.
  • Data lineage.
  • Metadata management.
  • Master Data Management.
  • Data security and access control.
  • GDPR and EU data protection requirements.


AI & Analytics Exposure

The ideal candidate will understand how modern data architectures support:

  • Machine Learning.
  • Generative AI.
  • LLM applications.
  • RAG architectures.
  • Vector databases.
  • Feature stores.
  • AI/ML data pipelines.
  • Business Intelligence and advanced analytics.


Experience

  • 7+ years of experience in data engineering, data architecture or related roles.
  • Proven experience designing enterprise-scale data platforms.
  • Experience working with cloud-based data architectures.
  • Strong understanding of data modelling and data integration.
  • Experience working across multiple business and technology stakeholders.
  • Experience translating business requirements into scalable data architectures.
  • Experience leading technical architecture decisions.


Advantageous

  • TOGAF or equivalent architecture certification.
  • Cloud certification in AWS, Azure or GCP.
  • Databricks certification.
  • Snowflake certification.
  • Microsoft Fabric experience/certification.
  • Experience designing architectures for AI and GenAI.
  • Experience within regulated industries.
  • Experience working across multiple EU countries.
  • Understanding of GDPR, data sovereignty and EU regulatory requirements.


Key Competencies

  • Strategic and architectural thinking.
  • Strong technical leadership.
  • Excellent stakeholder management.
  • Strong communication and documentation skills.
  • Ability to balance architecture, scalability, security and cost.
  • Commercial understanding of enterprise technology.
  • Ability to work effectively with distributed international teams.
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