Data Engineer

Data Engineer

We’re looking for a Data Engineer to architect and scale the data backbone that powers our AI‑driven donor engagement platform. You’ll design and own modern, cloud‑native data pipelines and infrastructure that deliver clean, trusted, and timely data to our ML and product teams - fueling innovation that revolutionizes the nonprofit industry.

Givzey is a Boston-based, rapidly growing digital fundraising solutions company, built by fundraisers for nonprofit organizations. 

Join a fast-growing, mission-driven team working across two innovative platforms: Givzey, the first donor commitment management platform revolutionizing nonprofit fundraising, and Version2.ai, a cutting-edge AI platform helping individuals and organizations create their most authentic, effective digital presence. As an engineer at the intersection of philanthropy and artificial intelligence, you'll build scalable, high-impact solutions that empower nonprofit fundraisers and redefine how people tell their stories online. We’re a collaborative, agile team that values curiosity, autonomy, and purpose. Whether you're refining AI-driven experiences or architecting tools for the future of giving, your work will help shape meaningful technology that makes a difference.

• Design & build data pipelines (batch and real‑time) that ingest, transform, and deliver high‑quality data from diverse internal and third‑party sources
• Develop and maintain scalable data infrastructure (data lakes, warehouses, and lakehouses) in AWS, ensuring performance, reliability, and cost‑efficiency
• Model data for analytics & ML: create well‑governed schemas, dimensional models, and feature stores that power dashboards, experimentation, and ML applications
• Implement data quality & observability frameworks: automated testing, lineage tracking, data validation, and alerting
• Collaborate cross‑functionally with ML engineers, backend engineers, and product teams to integrate data solutions into production systems
• Automate infrastructure using IaC and CI/CD best practices for repeatable, auditable deployments
• Stay current with emerging data technologies and advocate for continuous improvement across tooling, security, and best practices
  

• US Citizenship
• Bachelor’s or Master’s in Computer Science, Data Engineering, or a related field
• 2+ years of hands-on experience building and maintaining modern data pipelines using python-based ETL/ELT frameworks
• Strong Python skills, including deep familiarity with pandas and comfort writing production-grade code for data transformation
• Fluent in SQL, with a practical understanding of data modeling, query optimization, and warehouse performance trade-offs
• Experience orchestrating data workflows using modern orchestration frameworks (e.g., Dagster, Airflow, or Prefect)
• Cloud proficiency (AWS preferred): S3, Glue, Redshift or Snowflake, Lambda, Step Functions, or similar services on other clouds
• Proven track record of building performant ETL/ELT pipelines from scratch and optimizing them for cost and scalability
• Experience with distributed computing and containerized environments (Docker, ECS/EKS)
• Solid data modeling and database design skills across SQL and NoSQL systems
• Strong communication & collaboration abilities within cross‑functional, agile teams
  

Nice‑to‑Haves

• Dagster experience for orchestrating complex, modular data pipelines
• Pulumi experience for cloud infrastructure‑as‑code and automated deployments
• Hands‑on with dbt for analytics engineering and transformation-in-warehouse
• Familiarity with modern data ingestion tools like dlt, Sling, Fivetran, Airbyte, or Stitch
• Apache Spark experience, especially useful for working with large-scale batch data or bridging into heavier data science workflows
• Exposure to real-time/event-driven architectures, including Kafka, Kinesis, or similar stream-processing tools
• AWS data & analytics certifications (e.g., AWS Certified Data Analytics - Specialty)
• Exposure to serverless data stacks and cost‑optimization strategies
• Knowledge of data privacy and security best practices (GDPR, SOC 2, HIPAA, etc.)
  

• Be part of a world‑class team focused on inventing solutions that can transform philanthropy
• Build & refine data pipelines that feed our Sense (AI) and Go (engagement) layers, ensuring tight feedback loops for continuous learning
• Own the full stack of data work - from ingestion to transformation to serving - contributing daily to our codebase and infrastructure
• Partner closely with customers, founders, and teammates to understand data pain points, prototype solutions, iterate rapidly, and deploy to production on regular cycles
• Help craft a beautiful, intuitive product that delights nonprofits and elevates donor impact