Sr. Data Engineer

The Role

As a Sr. Data Engineer at Bloomerang, you'll build the data foundation that powers the next decade of the Bloomerang Giving Platform—the BI dashboards, in-product reports, ML models, and AI agents—Penny, our AI fundraising partner, among them. Reporting to the Director of AI Product Engineering, you'll join an established team expanding the Unified Data Foundation (UDF): a Databricks lakehouse that brings together CRM, Fundraising, and Volunteer data into a single, well-modeled source of truth—one foundation, many consumers.

This is a hands-on, builder role. Data is the moat; intelligence is the castle. You'll design the pipelines, harden the models, and make the data observable enough that 24,000+ nonprofits can trust what they see. You'll partner daily with our data architects, AI and ML engineers, and platform engineering peers, and you'll bring AI-native habits into how you write, test, and reason about data systems.

What You Will Do

• Design and ship production pipelines on Databricks, using a medallion architecture (bronze → silver → gold / landing → curated → presentation) grounded in Data Vault 2.0 modeling patterns.
• Build and harden the curated and presentation layers—the unified domain model and the product- and reporting-facing views that drive donor lifetime value, retention, lapse risk, and campaign ROI.
• Resolve identity across products. Build and harden the matching that ties a single supporter together across CRM, Fundraising, and Volunteer—so donor lifetime value, retention, and lapse risk are computed on one trustworthy record, not three partial ones.
• Move us toward near-real-time data. Partner with our architects on Change Data Capture (Debezium on Kafka/MSK) so customers see donor activity sooner and analysts, Penny and other AI agents act on fresher signals.
• Integrate trusted external partners through clean, secure, observable pipelines.
• Make data observable. Extend our existing tracing and AI lifecycle tooling (Honeycomb, MLflow, Langfuse) into ETL, so we catch tenant-level failures before customers do.
• Partner with AI and product engineers to make sure the right data is in the right shape at the right time for Penny and the products that depend on her.
• Use AI tools (Claude Code, Cursor, or similar) daily for pipeline development, schema design, code review, and problem-solving. We expect this to fundamentally change how you build, not just speed up what you'd build anyway.
• Raise the bar on engineering standards—testing, idempotency, documentation, security, and the boring rigor that keeps data trustworthy at scale. We treat data pipelines as software — code review, SemVer, CI/CD via Databricks Asset Bundles — and we hold data engineering to the same standards as our application teams.