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Data Engineer - Basel

ekona.ai · Bâle

Nouveau
Onsite Senior 🇬🇧 English
SQL Python dbt Airflow Snowflake Dagster Prefect Databricks BigQuery Git CI/CD Terraform SAP Neo4j

Description du poste

About the role

ekona builds production-grade AI systems and applications for clients in life sciences, supply chain, consumer goods and finance. It is looking for a Data Engineer in Basel who owns the data foundation end to end: turning messy master data, ERP extracts, product hierarchies and documents into reliable models and decisions. The role is full time, permanent and on site, with time spent at client sites.

Key responsibilities

  • Design and run ELT pipelines with dbt and Airflow on Snowflake and other cloud data platforms
  • Model data into clean, documented and tested marts used by applications and AI agents
  • Profile and reconcile data across sources and environments, and build data-quality checks into the pipeline
  • Work with ERP and PLM systems such as SAP, master data, product hierarchies and bills of materials, plus unstructured spreadsheets and documents
  • Feed planning and simulation tools, forecasting models, knowledge graphs and retrieval systems alongside AI and software engineers
  • Use version control, code review, CI/CD and infrastructure as code, and help build the data engineering playbook, templates and tooling

Required profile

  • Several years of professional data engineering experience with production pipelines
  • Solid data modeling and the judgment to keep models simple
  • Ability to talk to non-technical stakeholders and turn their questions into data-driven answers
  • Very good English, spoken and written; German is a plus

Required skills

  • Strong SQL and Python
  • Hands-on experience with dbt, Airflow and Snowflake, or close equivalents such as Dagster, Prefect, Databricks or BigQuery
  • At least one major cloud platform (GCP, Azure or AWS), Git workflows, CI/CD and containers; Terraform is a plus
  • Nice to have: SAP data (S/4HANA, material master, BOM, PLM), Databricks, graph databases such as Neo4j, feature pipelines and embeddings for ML and LLM applications, and experience in consumer goods, life sciences, supply chain or finance

Questions fréquentes

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