Junior Data Engineer

Imagine using your data engineering skills to improve the way cardiovascular health is monitored and managed. Hilo by Aktiia is redefining blood pressure monitoring through AI-driven optical technology built on more than 20 years of research at the Swiss Center for Electronics and Microtechnology (CSEM). Their solution combines a wearable device (Hilo), a mobile app, and a cloud-based platform for healthcare professionals — empowering users and physicians with continuous, actionable insights into blood-pressure patterns.

With more than 200,000 users, over $120M in funding, and a CE-certified medical device already available across several markets, Aktiia is continuing to scale its technology, product ecosystem, and international presence.

As Aktiia expands its data capabilities, we’re looking for a Junior Data Engineer who will help scale a Databricks-based medallion Lakehouse from a primarily R&D/Core Tech setup into a broader company-wide data platform. Working closely with and learning directly from an experienced Senior Data Engineer, you will support the ingestion, centralization, documentation, and validation of clinical, legacy, and operational data. Your work will help transform complex and sometimes messy data sources into clean, reliable, and model-ready pipelines used by Data Science, ML, Algorithm, and Core Tech teams.

• 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…