Junior Data Engineer

Responsibilities

• Data Ingestion & Lakehouse Development:
  Design, build, and maintain data ingestion and processing pipelines within Aktiia’s Databricks-hosted medallion Lakehouse, working under senior guidance while gradually taking on more ownership.
• Clinical & Legacy Data Integration:
  Take an active role in ingesting, centralizing, and documenting clinical data currently spread across EDC platforms such as Castor and Red Cap, databases, standalone archives, and other historically grown sources.
• Data Exploration & Practical Data Archaeology:
  Work hands-on with unfamiliar and sometimes messy datasets, identify structures and inconsistencies, and turn unstructured situations into reliable, usable data assets.
• Pipeline Development & Preprocessing:
  Build preprocessing and ETL/ELT pipelines that provide clean, structured, and model-ready datasets for Algorithm Development, Core Tech, Machine Learning, and Data Science teams.
• Data Quality, Validation & Documentation:
  Define and apply practical standards for data quality, validation, traceability, and documentation — especially for sensitive and clinically relevant datasets.
• Observability & Engineering Practices:
  Implement logging, validation checks, alerting, and basic observability for new pipelines, while contributing to shared codebase practices such as Git, code reviews, CI/CD, and testing.
• Platform Scaling &
  
  Collaboration:
  
  Support the evolution of the lakehouse from selected technical use cases toward a company-wide data infrastructure, working closely with the Senior Data Engineer, ML Engineers, Data Scientists, and cross-functional stakeholders.

• Academic Background
  :
  Bachelor’s degree or higher in Computer Science, Data Engineering, Data Science, Software Engineering, or a related technical field.
• Professional Expertise
  :
  At least 1+ year of practical post-study experience in data engineering, data infrastructure, data ingestion, or a similar hands‑on technical role. You may still be early in your career, but you have already worked with real data pipelines, production‑oriented data workflows, or collaborative data platforms.
• Technical Experience
  :
  You have solid hands‑on experience with Python and SQL and bring practical experience with Databricks. You are familiar with cloud environments — ideally AWS. You understand the basics of ETL/ELT pipeline design, data ingestion patterns, and data modelling, and you have worked with shared codebases using Git, code reviews, CI/CD, or testing practices. Experience with Spark, Delta Lake, or Parquet is a strong plus.
• Industry Fit
  :
  Ideally, you have gained experience in a start‑up, scale‑up, or technically demanding environment where you worked hands‑on across the data pipeline. Exposure to regulated or data‑sensitive industries such as Med Tech, Pharma, Fin Tech, or healthcare is a plus, especially when it comes to data quality, validation, and documentation.
• Language Skills
  :
  English at a highly proficient level is a must. French is advantageous.
• Personality
  : A curious, pragmatic, and hands‑on problem‑solver, you enjoy bringing structure into complex data environments. You work independently, ask the right questions, stay well organized, and collaborate openly with Data Engineering, Data Science, ML, Algorithm, Core Tech, and Product stakeholders. You are based in Switzerland and are comfortable working in a hybrid setup with one day per week regular presence in Neuchâtel.