About the job
QED AI is an innovative technology firm dedicated to enhancing public health and food security in Sub-Saharan Africa. By developing cutting-edge digital infrastructure and leveraging AI, we operate at the critical intersection of humanitarian aid and scientific research. Our initiatives include monitoring diseases such as HIV, malaria, and tuberculosis, as well as conducting nutrient analyses for crops and soils across various African nations. Our funding sources include prestigious philanthropic and governmental organizations such as the Global Fund, Gates Foundation, and the CDC.
We are seeking a mid/senior Data Platform Engineer (Backend) to join our dynamic team in Warsaw. Ideal candidates will possess the following:
Proven experience in designing and maintaining data pipelines utilizing ETL and/or ELT methodologies, with a critical eye on trade-offs rather than adhering to a single pattern.
Solid understanding of data pipeline reliability, including concepts of idempotency, backfills, and the management of late or corrected data.
Ability to structure data systems into distinct layers (e.g., raw, cleaned, curated) and articulate their varying functions and guarantees.
Experience with making informed decisions between batch, micro-batch, and streaming methods based on latency, accuracy, and operational complexity.
Strong foundation in software engineering principles, version control, writing clean code and tests, robust design, and a grasp of basic data structures and algorithms.
Ability to conceptualize logical software architectures and communicate clearly, both verbally and in writing.
Eagerness to engage with a diverse range of problems and technologies.
Willingness to participate in regular design sessions, code reviews, and collaborate effectively within teams.
Experience with UNIX-based or OSX-Darwin development environments.
Working proficiency (≥C1) in English, both spoken and written (minimum typing speed of 45 words per minute).
Interest in collaborating with individuals from different cultural backgrounds.
Demonstrated emotional resilience and social intelligence.
A genuine passion for your work and a generally optimistic outlook, while recognizing the value of constructive skepticism.
Additional skills that are advantageous but not mandatory include:
Understanding of analytical data modeling concepts, including hierarchical and dimensional modeling.

