Senior Data Platform Engineer

QED is a tech company focused on public health and food security in Sub-Saharan Africa. They build digital infrastructure and AI applications for aid and scientific research, including surveillance of HIV, malaria, TB, and nutrient analysis of crops and soils at national scale in several African countries. Their funding comes from philanthropic and governmental organizations such as the Global Fund, Gates Foundation, and the CDC.

• Experience designing and maintaining data pipelines with ETL/ELT methods
• Understanding of data pipeline reliability concepts like idempotency, backfills, late or corrected data handling
• Ability to structure data systems into raw, cleaned, and curated layers
• Knowledge to choose between batch, micro-batch, and streaming processing approaches
• Strong software engineering skills, including version control, code readability, testing, and software design
• Ability to conceive logical software architectures and communicate clearly in writing and verbally
• Willingness to tackle diverse problems and technologies
• Willingness to work in teams, participate in design sessions and code reviews
• Experience developing primarily on UNIX-based or OSX-Darwin platforms
• Working proficiency (≥C1) in English speaking, reading, and typing
• Emotional resilience and social intelligence
• Care about the quality of work and optimism

Nice to have:

• Understanding of analytical data modeling and defining clear data semantics
• Experience with Python, Django, DBT, Dagster, Luigi, or Clickhouse
• Experience with containerization and orchestration tools like Docker, Terraform, Kubernetes, or Nix
• Knowledge of data warehousing, OLAP, metadata management, dimensional modeling, relational database theory
• Participation in programming or math competitions
• Product-oriented mindset
• Interest in sustainable development goals in public health, agriculture, and developing countries

• Design and maintain data pipelines using ETL and/or ELT approaches, evaluating trade-offs between different patterns
• Ensure data pipeline reliability including idempotency, backfills, and handling late or corrected data
• Structure data systems into clear layers such as raw, cleaned, and curated datasets
• Decide between batch, micro-batch, and streaming data processing based on latency, correctness, and operational complexity
• Develop software using version control, readable code, tests, robust design, and understanding of data structures and algorithms
• Participate in regular design sessions, code reviews, and teamwork
• Develop primarily on UNIX-based or OSX-Darwin platforms

Requires full-time commitment physically based in Warsaw, Poland, with hybrid remote and in-person work. Expected office attendance is at least 2-3 days per week. Flexible working hours allow personal time during the day. Personal data processing details and rights are disclosed as per the recruitment process legal information.