Staff Data Engineer

Fundraise Up is a global fundraising platform dedicated to enhancing the donation experience for nonprofits. As a Staff Data Engineer, you will own the technical architecture of the Data domain, developing data pipelines and establishing standards to ensure data integrity across teams.

Responsibilities:

• Develop and steward a 1–2 year technical roadmap for the Data Warehouse platform. Evaluate the efficiency of the current tech stack
• Build reliable ETL/ELT processes and develop scalable data pipelines for delivering data into a centralized analytical warehouse
• Own and document the key architectural decisions (ADRs) for data models, pipelines, and warehouse design; surface structural risks before they block delivery
• Serve as the technical counterpart for product and analytics teams: establish data engineering standards and governance and drive their adoption across teams, with the authority to block decisions that threaten data integrity
• Act as the senior technical escalation point for data-related incidents — drive resolution and implement systemic fixes that address root causes
• Write and optimize queries for MongoDB and ClickHouse
• Manage and maintain workflows in Airflow
• Mentor analysts and product teams to model and use data well, making your expertise reusable through patterns and documentation
• At Fundraise Up, AI is a default tool, not an experimental one. We expect you to actively use AI coding agents (Claude Code, Codex) in your day-to-day work — for building pipelines, querying, debugging, and exploration — and to grow your fluency as the tools evolve

Requirements:

• 12+ years as a Data Engineer, with a track record of owning data architecture decisions that held up over time
• Experience leading complex, cross-team technical changes — design, coordination, and driving to completion — across teams that don't report to you
• 5+ years with Python; strong with Kafka. Node.js is a plus
• Strong understanding of data processing algorithms and principles
• Hands-on experience with ClickHouse, Airflow, Amazon S3, Git, Docker
• Solid understanding of Data Lake and Data Warehouse architectures
• Experience working with large-scale data and query optimization
• Hands-on, daily use of AI coding agents (Claude Code, Codex) as a core part of how you work — not occasional experimentation
• Ability to work collaboratively toward shared goals
• Strong sense of ownership, responsibility, and proactivity
• English level: B2+
• Experience with Apache Parquet, MLflow, MongoDB
• Public technical communication — RFCs, ADRs, tech talks
• Experience in a distributed, remote-first engineering organization