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# AI Data Engineer

- **Company:** HERE Global B.V.
- **Location:** Berlin, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Cloud Engineering, Information Systems, Data as a Services, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Metadata, Meta-Data Management, Metadata Repositories, Search Technologies, Software Engineering, SQL Databases, Unstructured Data, Enterprise Data Management, Azure Data Factory, Snowflake, Generative AI, Event Driven Architecture, AI Platforms, Kubernetes, Information Technology, Data Lineage, Enterprise Integration, Integration Frameworks, Apache Kafka, Data Management, Data Pipelines, Databricks, Programming Languages
- **Published:** September 30, 2026
- **Apply:** https://careers-here.icims.com/jobs/81884/lead-ai-data-engineer/job?mode=apply&apply=yes&in_iframe=1&hashed=-336061842

## About the Role

* Bachelor's/ Master's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or a related field.
 * 5+ years of experience in data engineering, integration engineering, or data platform development.
 * Experience designing and building enterprise-scale data pipelines using modern cloud and data platform technologies.
 * Strong understanding of data modeling, ETL/ELT processes, APIs, event-driven architectures, and data integration patterns.
 * Experience working with both structured and unstructured data sources.
 * Proficiency with SQL and at least one modern programming language such as Python, Java, or Scala.
 * Experience working with AWS cloud platform and kubernetes
 * Strong problem-solving skills and the ability to work across complex organizational environments.
 * Experience supporting AI, machine learning, generative AI, or agentic AI solutions.
 * Familiarity with vector databases, embeddings, semantic search, and Retrieval-Augmented Generation (RAG) architectures.
 * Experience with data governance, metadata management, data catalogs, and data lineage tools.
 * Understanding of data privacy, security, and responsible AI principles.
 * Experience with modern orchestration and integration tools such as Airflow, Databricks, Snowflake, Azure Data Factory, Kafka, or similar platforms.
 * Experience building enterprise APIs and reusable data services.
 * Knowledge of M365 Copilot, Copilot Studio, Azure AI Foundry, AWS Bedrock, or similar AI ecosystems., * Systems thinker who can connect business outcomes to underlying data capabilities.
 * Pragmatic problem solver who balances speed of delivery with governance and quality.
 * Passionate about unlocking the value of enterprise data through AI.
 * Strong communicator capable of working with executives, business leaders, architects, and engineering teams.
 * Comfortable operating in ambiguous environments and helping shape emerging AI capabilities.

## Description

* Design, build, and maintain scalable data pipelines that support AI, machine learning, generative AI, and agent-based solutions.
 * Develop connectors and integrations across enterprise applications, databases, SaaS platforms, APIs, and knowledge repositories.
 * Create reusable data services and data products that accelerate AI use case delivery across the company.
 * Ensure AI systems can access high-quality, current, and trusted information through robust retrieval and integration patterns.

Enable AI Solutions at Scale

 * Partner with AI Solution Architects, Data Scientists, Product Owners, and Business Stakeholders to understand data requirements for AI use cases.
 * Design and implement architectures supporting agentic workflows, Retrieval-Augmented Generation (RAG), semantic search, and AI-driven automation.
 * Establish standardized patterns for structured and unstructured data ingestion, transformation, and access.
 * Support the transition of AI solutions from pilot initiatives to enterprise-scale production deployments.

Improve Data Quality and Governance

 * Identify and resolve data quality, consistency, lineage, and ownership issues across business domains.
 * Implement monitoring, validation, and observability mechanisms to ensure data reliability.
 * Work with Security, Privacy, and Governance teams to ensure AI solutions comply with company policies and regulatory requirements.
 * Define and maintain metadata, cataloging, and governance standards that improve discoverability and trust in enterprise data., * Partner with business, technology, and data teams to prioritize data readiness initiatives supporting the AI roadmap.
 * Influence platform and application owners to adopt AI-friendly data practices.
 * Help establish enterprise best practices for AI data architecture, governance, and operational support.
 * Contribute to the broader AI CoE strategy by identifying systemic data challenges and recommending long-term solutions.