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 for aid and scientific inquiry, 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 CDC.

• Experience designing and maintaining data pipelines using ETL and/or ELT
• Understanding of data pipeline reliability concepts such as idempotency and backfills
• Ability to structure data systems into layers and reason about their purposes
• Experience with batch, micro-batch, and streaming data processing approaches
• Strong software engineering background including version control, readable code, tests, and algorithms
• Ability to conceive logical software architectures and communicate clearly
• Willingness to tackle diverse problems and technologies
• Development experience on UNIX-based or OSX-Darwin platforms
• Working proficiency in English (≥C1) in speaking, reading, and typing
• Willingness to work with people from other cultures
• Emotional resilience and social intelligence

Nice to have:

• Understanding of analytical data modeling and transforming raw data into analytics-ready datasets
• Experience with Python, Django, DBT, Dagster, Luigi, or Clickhouse
• Experience with containerization (Docker), Terraform, Kubernetes, or Nix
• Knowledge of data warehousing, OLAP, metadata management, dimensional modeling, and relational database theory
• Experience with programming and/or math competitions
• Product-oriented mindset
• Willingness to go on an adventure
• Domain knowledge or interest in sustainable development goals, public health, agriculture, and assisting developing countries

• Design and maintain data pipelines using ETL and/or ELT approaches, reasoning about trade-offs
• Ensure data pipeline reliability including idempotency, backfills, and handling late or corrected data
• Structure data systems into clear layers (raw, cleaned, curated) and reason about their purposes and guarantees
• Decide between batch, micro-batch, and streaming approaches based on latency, correctness, and operational complexity
• Participate in regular design sessions, code reviews, and teamwork

Requires a full-time commitment physically based in Warsaw, Poland, with a hybrid of remote and in-person work throughout the week. At least 2-3 days per week in the office are expected.