Lead AI Data Engineer
Lead a team to build AI-ready data pipelines and integrations that power HERE’s AI, generative AI, and agent-based solutions, ensuring trusted, scalable data foundations for enterprise use cases.
- Compensation
- Not disclosed
- Location
- Krakow, PL
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Contract
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 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.
Full description
What's the role?
We are looking for a strong AI Data Engineer to help make HERE data AI-ready for the CoE's portfolio of use cases, building the pipelines, integrations, data products, and governed access patterns that enable AI solutions, agents, and automations to operate on trusted information. This role works across enterprise systems, platforms, and business domains to resolve data quality, fragmentation, metadata, and access challenges that often prevent AI initiatives from moving beyond pilot stages into scalable production deployments.
As a critical member of the AI Center of Excellence, the AI Data Engineer transforms data readiness from a recurring executive concern into a measurable organizational capability, ensuring AI solutions are built on reliable, secure, and accessible data foundations.
What You'll Do
Build AI-Ready Data Foundations
- 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.
Drive Enterprise Collaboration
- Partner with business, technology, and data teams to prioritize data readiness initiatives supporting the AI roadmap.