Junior Data Engineer

At knok, we dare to lead and humanise the digital transformation of healthcare. We envision a world where everyone has timely access to quality healthcare through digital technology, creating a more equal society. We genuinely believe in it, and you can recognise it in every person who embraces this mission.

Through a Digital Front Door strategy, knok connects patients, providers and healthcare professionals in one place. Our API-first white-label platform enables a continuous, engaging and personalised healthcare experience for all conditions through a cutting-edge Patient Journey Engine.

With regular clinical practice as our main source of knowledge, we leverage ready-to-use data to improve care automation and increase financial savings. Since 2015, we have enabled more than 2.5 million clinical interactions in over 12 countries. Our platform is scalable and AI-ready, enhancing the power of data-driven care to deliver better outcomes during all stages of life.

Are you ready to join us in revolutionising healthcare and making a tangible impact on people's lives?

We are looking for a Junior Data Engineer to join our Data team and help ensure that the data powering knok remains accurate, reliable and available when our teams need it.
You will work closely with more experienced Data Engineers, contributing to the maintenance of our data platform, monitoring daily data pipelines and transforming healthcare data into structured and valuable information.
This is an opportunity to develop your Data Engineering skills while working with real healthcare data and systems that directly support clinical and business decision-making.
If this makes sense, keep reading!

As a Junior Data Engineer, you will:

• Support the maintenance and continuous improvement of our data platform;
• Ensure data remains accurate, reliable and consistently up to date;
• Monitor our daily ELT processes, ensuring pipelines run successfully and complete within the expected timeframe;
• Identify and investigate data quality issues, pipeline failures and inconsistencies;
• Transform raw data from different sources into structured and usable datasets;
• Work with healthcare data, including HL7, extracting meaningful information from areas such as laboratory results, appointments and admissions;
• Support the development and maintenance of data models and transformations;
• Collaborate with Data Analysts and other teams to understand data needs and provide reliable solutions;
• Communicate technical topics clearly and practically to non-technical stakeholders;
• Contribute to documentation, testing and continuous improvement of our data processes.

To be considered for this role, here are the skills we’re looking for:

• Degree in Computer Science, Data Science, Information Technology, Engineering or a related field;
• Initial academic or professional experience with Python;
• Good knowledge of SQL and relational databases;
• Basic understanding of data pipelines, ETL/ELT concepts and data modelling;
• Strong analytical and problem-solving skills;
• Attention to detail and a strong focus on data quality;
• Ability to investigate problems and work through them in a structured way;
• Ability to communicate technical concepts clearly to non-technical stakeholders;
• Strong willingness to learn and develop your Data Engineering skills;
• Collaborative mindset, curiosity and sense of ownership.

• Have previous experience with orchestration tools such as Airflow;
• Have worked with dbt;
• Have experience with cloud data platforms such as BigQuery, Snowflake or Redshift;
• Have worked with version control tools such as GitHub;
• Have previous exposure to HL7 or other healthcare interoperability standards;
• Have experience or a strong interest in healthcare data.

1. People Interview: A conversation to get to know you better, explore your background and motivation, and introduce you to knok, our culture and the role.
2. Hiring Manager Interview: A conversation with the Hiring Manager to explore your technical foundations, previous projects and approach to data-related problems.
3. Case Study: A practical exercise designed to understand how you approach a data engineering problem, structure your thinking and work with data.
4. Final Interview: A conversation to discuss your case study, explore your technical decisions and better understand how you approach learning, collaboration and ownership.