Lead AI Data Engineer

HERE Technologies is a company located in Kraków, focusing on AI data engineering within its AI Center of Excellence, working on data readiness to support AI solutions, agents, and automations.

• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or related field.
• 5+ years experience in data engineering, integration engineering, or data platform development.
• Experience designing and building enterprise-scale data pipelines with modern cloud and data platform technologies.
• Strong understanding of data modeling, ETL/ELT processes, APIs, event-driven architectures, and data integration patterns.
• Experience with 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 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 orchestration and integration tools such as Airflow, Databricks, Snowflake, Azure Data Factory, Kafka, or similar.
• Experience building enterprise APIs and reusable data services.
• Knowledge of AI ecosystems like M365 Copilot, Copilot Studio, Azure AI Foundry, AWS Bedrock, or similar.

Build AI-Ready Data Foundations:

• Design, build, and maintain scalable data pipelines supporting 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 accelerating AI use case delivery.
• Ensure AI systems can access high-quality, current, and trusted information.

Enable AI Solutions at Scale:

• Partner with AI Solution Architects, Data Scientists, Product Owners, and Business Stakeholders to understand data requirements.
• 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 transition of AI solutions from pilot to enterprise-scale production.

Improve Data Quality and Governance:

• Identify and resolve data quality, consistency, lineage, and ownership issues.
• Implement monitoring, validation, and observability mechanisms.
• Work with Security, Privacy, and Governance teams to ensure compliance with policies and regulations.
• Define and maintain metadata, cataloging, and governance standards.

Drive Enterprise Collaboration:

• Partner with business, technology, and data teams to prioritize data readiness initiatives.
• 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 AI CoE strategy by identifying systemic data challenges and recommending solutions.

Informacja o administratorze danych i prawach osób aplikujących zgodnie z RODO, w tym prawo do dostępu, sprostowania danych oraz informacji o przetwarzaniu danych osobowych w celu rekrutacji.