Sr. Data Engineer

At Bloomerang, we believe change happens on purpose. We champion the power and potential of nonprofits, igniting next-level impact with the team and technology built for purpose. Our powerful giving platform and stellar support enable tens of thousands of nonprofits to raise more, recruit more, and retain more, fueling maximum impact and raising the bar on what’s possible for the nonprofit sector.

That's why, even as the nonprofit sector sees declines in giving, Bloomerang customers raise more year over year.

We're also in the business of creating thriving employees. Join a mission-driven culture built on our core values of Simplify, Care and Act. We know our people are the key to our success, and we're proud to be home to some of the most innovative and skilled individuals in the workforce today. Come feel invigorated and unstoppable with us!

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.

• 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  treat data pipelines as software — code review, Sem Ver, CI/CD via Databricks Asset Bundles — and we hold data engineering to the same standards as our application teams.

• Modern data platform experience: 5+ years building production data pipelines on a modern lakehouse or warehouse. Databricks w/ Unity Catalog strongly preferred; we’ll consider Snowflake, Big Query, or equivalent if your relevant data engineering skills travel.
• Identity resolution: experience matching and merging records across systems—entity resolution, dedupe/merge, or master‑data "golden record" work—especially where there’s no shared key to join on.
• Strong SQL and strong Python (or Scala). Comfort with PySpark is a plus.
• Data modeling fluency: working knowledge of dimensional and/or Data Vault 2.0 patterns. You can defend a schema decision and explain the trade‑offs.
• Production sensibility: real experience…