Data Engineer

About the job

The Data Platform team is responsible for running and maintaining the infrastructure in charge of managing large volumes of data in the healthcare environment. We seek to incorporate into this team a passionate about databases who helps the team to achieve an infrastructure that can scale and grow in a safe and robust way to achieve all business objectives.

You will collaborate with the rest of the tech teams (specially with DataOps and DevOps team) by analyzing and understanding data sources, participating in the design, and providing information and guidance on database technology and data modeling best practices.

About you

You will be working in a very dynamic environment, which means that you must have a strong problem-solving ability, organization skills, and be proactive. 

In terms of personality, we are looking for someone who enjoys working in a wide range of areas, adapts quickly to new situations and don’t be afraid to step out of comfort zone.

Task

You will have to feel comfortable giving support to the rest of the team and you can definitely expect to:

• Query, model and deploy with PostgreSQL.

• Design, implement, deploy & test microservices.

• Create custom docker images and K8s deployments.

• Testing, logging, measuring and alerting of the deployed services.

• Monitor the existing metrics, analyze data and collaborate with other teams in an effort to identify and implement system and process improvements.

• Take care of database maintenance tasks, including:

  • Monitor database grow & bloat contention.
  • Backup monitoring, maintenance and test.

• Help other dev team members with query optimizations & data modeling. 

What we are looking for

• At least 3 years of working experience working in a data engineering department, preferably as a Data Engineer.
• Demonstrate proficiency in programming (plpgsql and Python will be your main tools of the trade).
• Proven skills of putting microservices to use, and you’re comfortable with working with containers in a cluster architecture (we use Docker & Kubernetes).
• Knowledge of relational database modeling concepts, proficiency in query performance tuning, and desire to share knowledge with others.

What we offer

• Full-time permanent contract.
• Company profit-sharing scheme valued at up to €10,000 per year
• Flexible schedule, with the possibility of home office. 
• 28 days of vacations per year.
• A warm, transparent and supportive team, with a huge emphasis on work-life balance. 
• The opportunity to make your mark in e-health and AI. 

About IOMED 

IOMED is a leading technology start-up company in the field of massive medical data extraction and processing. It is made up of a united, young, dynamic, and flexible team, which, after several years of working together, is characterized by its agility, enjoyment of work, and rapport. Accordingly, we are looking for candidates with the desire to innovate, launch a relevant project, and grow with it, always enjoying teamwork and challenges, which are many, every day.

As an employer, we offer equal opportunity. We want to grow our team with talented, dynamic people who want to make their mark in the field of AI and research, regardless of race, color, religion, national origin, gender, physical disability, or age.

What we do 

Nowadays, about 50% of clinical trials are delayed due to slow data collection from patients, which is done manually. As a result, clinical research is highly inefficient in both time and cost, taking years and billions of dollars to develop a new drug.

This problem could be solved through Real-World Data, that is, those contained in electronic medical records (EHRs). But unfortunately, up to 85% of existing clinical data is unstructured.

IOMED has found a solution to this situation, making it possible to fully harness the value of Real-World clinical data. We developed a tool that extracts the necessary data from clinical texts, resulting in a structured, standardized, and interoperable database that contains complete clinical information from hospitals.

In this way, non-reusable information is transformed into data available for Clinical Research, which allows increasing the number of patients that meet the criteria, as well as reducing the total time and manual work dedicated to this task.