• Provide IT Services for implementation of the clients cloud-based Software as a Service (SaaS) solution, software licensing, and maintenance. • Handle scope and communication to clients during delivery. • Ensure the database design of all components has an enterprise view and that each component/module works together to address the functional requirements. • Manage data warehousing (both traditional and cloud) and data governance. • Implement and perform performance tuning. • Utilize data engineering and ETL tools from multiple sources. • Implement self-service business intelligence tools. • Maintain in-depth knowledge of cloud technologies, platforms, and solutions as they relate to data architecture. • Work within an integrated and multi-cloud environment. • Provide data architecture expertise in building the solution. • Use data governance tools and develop data migration strategies from legacy to modern technologies. • Migrate documents and metadata from legacy applications to future state content management. • Collaborate with project stakeholders to ensure and address all functional and non-functional requirements.

• Design and plan new platform data solutions through collaboration with our Staff Data Engineer, product team, and internal stakeholders using RFC and Tech Spec documents • Collaborate with your peers to execute and implement high-quality software • Collaborate with your peers to assist in planning and execution of new software features, bringing a focus on data platform requirements • Drive the execution of your work from spec document to delivery through our SDLC • Grow your skillset and be a force multiplier by sharing knowledge through code reviews, pair programming, 1-1 conversations, and broader team trainings • Develop subject matter expertise in the Beam data ecosystem and our reporting platform through code development, directly supporting our Client Strategy team, Business Operations, and our partners • Understand and strive to balance tech debt with practicality while designing solutions • Gain knowledge of the nonprofit giving and e-commerce enablement spaces • Foster a team culture around Beam’s values of community, inclusivity, care, accountability and support • Strive for continuous improvement through goal setting, feedback, and other growth opportunities

• Own data pipelines and infrastructure: Design, implement, and evolve scalable, production-grade systems using tools such as Dagster, Airflow, Snowflake, AWS, MongoDB, and dbt. Apply a cloud-native and DevOps mindset using CI/CD, infrastructure-as-code, monitoring, and automated testing to build reliable systems. Partner with cross-functional teams to deliver solutions that meet both immediate product needs and long-term organizational strategy. • Lead data ingestion and integration: Bring in complex, high-volume datasets while ensuring strong data contracts, freshness, quality, integrity, and lineage, and build systems that empower domain experts to contribute to and maintain their own data pipelines. • Transform raw data into trusted data products: Convert raw inputs into structured, usable datasets that empower our analytical and product teams. Collaborate closely with operational data managers to ensure data models and intuitive, reliable alignment with how data is consumed in practice. • Leverage AI: Make informed judgement calls about how AI can be a force-multiplier for both your own work and the team’s and how it can’t. • Elevate the team: Mentor engineers, actively shape technical direction through architectural reviews and roadmap planning, and build team culture through documentation and knowledge sharing.

• Own end-to-end data migration execution for enterprise and multi-location customers from discovery through cutover and validation • Lead client-facing data discovery and data mapping sessions to define source structures, transformation rules, and migration scope • Profile, cleanse, normalize, deduplicate, and transform large datasets using Python and Pandas • Write advanced SQL queries (MySQL and PostgreSQL) to validate, transform, and reconcile migrated data • Define and apply business rules for matching, merging, and record standardization • Build repeatable migration scripts and reusable data transformation workflows using version control best practices • Partner with Implementation, Solutions Engineering, Product, and Support to resolve data-related blockers and edge cases • Create clear technical documentation including data mapping specs, transformation logic, validation plans, and migration runbooks • Design and execute data validation and integrity checks before and after migration • Identify risks early and define rollback or remediation approaches when needed • Contribute to continuous improvement of MoeGo’s migration tooling, standards, and playbooks