Bloomerang

Platform Engineer

• Build and own the agentic harness Bloomerang engineering runs on—agent workflows, tool and model integrations, orchestration, and the guardrails and evaluation infrastructure that keep AI-driven development reliable and safe.
• Use the throughput AI-driven development unlocks to follow through on delivery—ship minimum viable increments, then gather real customer feedback, run A/B tests, measure what shipped, and iterate until it makes a meaningful difference for nonprofits.
• Own cross-cutting AI-platform concerns while standardizing prompt and context pipelines and consolidating local development environments across our products and teams.
• Continually evaluate new models, AI tools, harnesses, and approaches—running rigorous evals and turning the winners into paved-road tooling the whole organization can rely on.
• Design, build, and own backend services at the core of our platform—in the near term, our authentication chain (centralized authentication and session management), advancing toward a unified identity solution, primarily in C#/.NET.
• Diagnose and resolve complex technical bottlenecks across distributed services and the AI tooling that sits on top of them.
• Improve the engineering standards, automation, developer tooling, and AI harness the team relies on.
• Help the team deliver in a continuous-flow (Kanban) model.
• Use your experience to shape our software and delivery tooling—raising the bar on engineering standards, teaching and learning alongside the organization, and helping us ship better products for the nonprofits we serve.
• Actively explore, integrate, and help lead the responsible use of AI across the team.

Senior Software Engineer – Platform, AI

Data Engineer

• Lead the end-to-end design, development, and implementation of enterprise data warehouse solutions supporting corporate analytics
• Own the migration and refactor of Bloomerang’s data warehouse from Google BigQuery to Databricks, supporting all internal analytics use cases
• Establish and maintain data warehouse standards, guidelines, and best practices across the organization
• Design and build scalable ETL and ELT pipelines that ingest, transform, and integrate data from internal systems, including MySQL, PostgreSQL, Salesforce, and more
• Work closely with analytics engineers to develop dimensional data models and enterprise data architecture that support reporting and business intelligence needs across the company
• Partner with cross-functional stakeholders to translate business requirements into scalable, well-governed data solutions
• Build automated data quality monitoring, validation, and governance processes to ensure accuracy and reliability of enterprise data
• Optimize data warehouse performance, reliability, and cost across cloud environments
• Create and maintain documentation for data architecture, pipelines, and data warehouse standards
• Mentor team members and collaborate with engineering on data warehouse best practices

• Design and ship production pipelines on Databricks, using a medallion architecture grounded in Data Vault 2.0 modeling patterns.
• Build and harden the curated and presentation layers—driving donor lifetime value, retention, lapse risk, and campaign ROI.
• Resolve identity across products to compute values on one trustworthy record.
• Move toward near-real-time data and partner with architects on Change Data Capture.
• Extend existing tracing and AI lifecycle tooling into ETL.
• Partner with AI and product engineers to ensure data is properly shaped for applications.
• Use AI tools in daily development tasks.
• Raise the bar on engineering standards, addressing testing, idempotency, documentation, and security.

Senior Data Engineer

Data Scientist

• Design and run the experimentation engine for fundraising actions.
• Build predictive and forecasting models that drive donor lifetime value and retention.
• Own model quality and evaluation for trustworthy AI products.
• Manage ML lifecycle on Databricks and MLflow including model training and performance monitoring.
• Define technical direction for data science with high standards and rigor.
• Partner daily with data engineers and AI engineers.

Lead Data Scientist

Full-stack Engineer

• Act as a driving force behind our mission to empower nonprofits
• Shape the technical direction of our SaaS platform
• Own the technical vision and direction for your domain
• Diagnose and resolve complex technical bottlenecks
• Champion Agile ceremonies and workflows
• Conduct rigorous code reviews
• Champion AI-driven development
• Serve as the technical lead, guiding the development of the team

Senior Software Engineer – Tech Lead

Product Manager

• Own Outcomes for a Customer Persona
• Stay Close to Customers
• Identify Problems Worth Solving
• Build, Prototype, and Experiment
• Deliver Outcomes, Not Features
• Operate as an AI-Native Product Manager
• Hold a High Quality Bar
• Create Clarity and Focus

Senior Product Manager

Data Analyst

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