Job Description

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.

Your Role

• 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 RedCap, 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.

Your Profile

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.

Technical Experience

• Solid hands-on experience with SQL and practical experience with Databricks.
• Familiar with cloud environments — ideally AWS.
• Understand the basics of ETL/ELT pipeline design, data ingestion patterns, and data modelling; experience with Git, code reviews, CI/CD, or testing practices.
• Experience with Spark, Delta Lake, or Parquet is a strong plus.

Industry Fit

Experience in a start-up, scale-up, or technically demanding environment, ideally within regulated or data-sensitive industries.

Language Skills

English at a highly proficient level is a must. French is advantageous.

Personality

A pragmatic, hands-on problem-solver who enjoys bringing structure into complex data environments, working independently, and collaborating openly.

Do you want to grow your expertise in Data Engineering and cloud-based lakehouse architectures while contributing to health technology? Then we’re excited to meet you!