Bachelor's degree in Computer Science, Engineering, Statistics, or a related field; equivalent demonstrated experience considered.
2 to 4 years in data engineering or backend engineering with substantial data work, in a production environment.
Strong Python and SQL; you write pipelines as software, with tests, not as scripts.
Solid PostgreSQL; exposure to SQL Server is a plus.
Hands-on dbt experience and familiarity with a modern orchestrator; Dagster preferred, Airflow background welcome.
Working understanding of incremental and idempotent load patterns; familiarity with Kafka and Spark is a valued plus, not a requirement.
Git discipline, CI/CD, and containerization (for example Docker) as daily habits; documentation you would want to inherit.
Awareness of handling sensitive personal data responsibly; experience with regulated, financial, or humanitarian data is a strong plus.
Evidence that you seek and act on feedback; this role comes with structured mentorship, and we are looking for someone who will use it.
Ability to explain what a pipeline does, what broke, and what you changed, clearly and in writing.
Instills Trust - Follows through on commitments, builds credibility by being direct and truthful, and shows genuine care for staff members.
Acts with Courage - Steps up to address difficult issues and speaks openly with bravery; takes the initiative to pursue new opportunities; takes full ownership of own work.
Makes Informed Decisions - Seeks relevant data and input when needed, takes appropriate action within their area of responsibility, and knows when to escalate.
GK
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