AI Data Engineer Lead - Scalable AI Data Foundations (Hybrid)

HERE Technologies; Kraków; Hybrid; PLN 180,000 - 280,000; Full time

Benefits offered by this job

Hybrid model

Competitive salary

Medical coverage for you and family (‑

Flexible hours

Team events

Job summary

HERE Technologies is seeking a strong AI Data Engineer to make HERE data AI-ready, building pipelines, integrations, data products, and governed access patterns for AI solutions. You will enable scalable production deployments across enterprise systems and platforms.

You will collaborate with AI Solution Architects, Data Scientists, and stakeholders to design architectures supporting RAG, semantic search, and agentic workflows while improving data quality, metadata, and governance across domains.

Qualifications

• Bachelor's/Master's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or 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 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 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

Responsibilities

• Build, design, and maintain scalable data pipelines for AI, ML, and agent-based solutions
• Develop connectors and integrations across enterprise apps, databases, SaaS platforms, APIs, and knowledge repositories
• Create reusable data services and data products to accelerate AI use case delivery
• Ensure AI systems access high-quality, trusted information via robust retrieval and integration patterns
• Partner with AI Solution Architects and Data Scientists to define data requirements for use cases
• Design architectures supporting agentic workflows, RAG, semantic search, and AI-driven automation
• Establish standardized patterns for data ingestion, transformation, and access
• Support transition of AI solutions from pilots to enterprise-scale deployments
• Identify data quality, lineage, and ownership issues and resolve them
• Implement monitoring, validation, and observability for data reliability
• Work with Security, Privacy, and Governance teams to ensure compliance
• Define metadata, cataloging, and governance standards to improve discoverability

Skills

SQL

Python

Java

Scala

Data modeling

ETL/ELT

APIs

Event-driven

Data integration

RAG

Education

Bachelor's/Master's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or related field

Tools

Airflow

Databricks

Snowflake

Azure Data Factory

Kafka

Kubernetes

AWS

Job description

HERE Technologies is seeking a strong AI Data Engineer to make HERE data AI-ready, building pipelines, integrations, data products, and governed access patterns for AI solutions. You will enable scalable production deployments across enterprise systems and platforms.

You will collaborate with AI Solution Architects, Data Scientists, and stakeholders to design architectures supporting RAG, semantic search, and agentic workflows while improving data quality, metadata, and governance across domains.