Data Engineer (AI)

Location: Fully remote

Company

Consulting organization supporting enterprise AI and data initiatives through reliable data foundations and scalable data products.

What you will do

• Design, build, operate, and improve enterprise ETL and ELT pipelines for AI, analytics, reporting, and agentic workflow use cases.
• Create clean, documented, reusable data products from operational, customer, logistics, sales, and other enterprise data sources.
• Implement data quality checks, monitoring, lineage, metadata, and reliability patterns for trustworthy production datasets.
• Integrate Azure-based AI services, Snowflake, APIs, file-based sources, and selected AWS data services.
• Translate business and AI use cases into data models, pipelines, and serving layers in collaboration with technical and business stakeholders.
• Onboard data sources with local IT and data owners while maintaining data protection, access control, and governance requirements.

Requirements

• Strong experience with Python, SQL, data modelling, ETL/ELT development, and production data pipelines.
• Experience with cloud-native data engineering, ideally on Azure; practical AWS exposure is beneficial.
• Experience with Snowflake or comparable cloud data platforms.
• Understanding of data quality, orchestration, observability, access management, and governance in enterprise environments.
• Ability to collaborate with data scientists, AI engineers, business stakeholders, and local IT teams in international settings.
• Comfort using GenAI software engineering tools such as GitHub Copilot, Cursor, or OpenCode.
• Fluent English required.

Nice to have

• Experience with Iceberg, AWS Glue, Spark, Databricks, or similar data engineering technologies.
• Experience with logistics, parcel operations, customer feedback, route, depot, or sales data.
• Exposure to AI feature stores, vector databases, retrieval pipelines, or data preparation for GenAI applications.

Culture & Benefits

• Fully remote work arrangement.
• Cross-functional collaboration with data scientists, AI engineers, cloud engineers, business process owners, and country IT teams.
• Work on enterprise AI initiatives and production-grade data products.