Sr. Data Engineer

Bloomerang is a company dedicated to empowering nonprofits through innovative technology and support. As a Sr. Data Engineer, you will build the data foundation for the Bloomerang Giving Platform, designing pipelines and ensuring data integrity for over 24,000 nonprofits.

Responsibilities:

• 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

Requirements:

• 5+ years building production data pipelines on a modern lakehouse or warehouse. Databricks w/ Unity Catalog strongly preferred; we'll consider Snowflake, BigQuery, or equivalent if your relevant data engineering skills travel
• 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
• Working knowledge of dimensional and/or Data Vault 2.0 patterns. You can defend a schema decision and explain the trade-offs
• Real experience operating pipelines in production—monitoring, alerting, and robust error handling
• You don't have to have led that build, but you should know what's hard about moving from batch to near-real-time
• You already use Claude Code, Cursor, or similar AI development environments as a daily part of how you build. You can speak to where they accelerate your work and where they don't
• You're energized by the pace of AI-driven change in how software—and data—gets built, and you bring that energy into your team
• You don't just write pipelines, you own outcomes. You build observability and testing within from day one
• A track record of working well with data scientists, ML engineers, or applied-AI teams. We have a Penny to feed
• Our customers trust us with their donors' data. You take that seriously
• Transformation framework experience generally (dbt, PySpark, etc.)
• AWS (S3, IAM, networking)
• Experience with observability tools like Honeycomb, OpenTelemetry, or MLflow
• Multi-tenant SaaS data experience
• Background in nonprofit, fundraising, or CRM data