Your Opportunity

 

DAFgiving360™ is an independent nonprofit organization created to increase charitable giving in the U.S. We offer a donor-advised fund program and related philanthropic tools and guidance that empower donors to incorporate charitable planning into their everyday lives and make a bigger difference in the world. Since our founding in 1999 as a 501(c)(3) public charity, DAFgiving360 donors have recommended over $50 billion in grants to more than 295,000 charities. DAFgiving360 has entered into a services agreement with Charles Schwab & Co., Inc. for administrative and other services, including human resources. This position will be an employee of Charles Schwab & Co., Inc. and will be subject to its policies and procedures but will report to and be accountable to DAFgiving360 for day-to-day activities.

Our Opportunity

The Senior Data Engineer is a senior technical contributor within DAFgiving360’s Data organization responsible for designing, building, operating, and evolving DAFgiving360’s modern data platform and engineering capabilities. This role serves as a key engineering owner of GIFT (Giving Insights, Foundational Trust), helping ensure that enterprise data is reliable, scalable, secure, and accessible to support analytics, operational decision-making, automation, and future AI initiatives.

Working closely with Analytics, Data Governance, Technology, Product, and business stakeholders, this individual will lead hands-on engineering efforts spanning data ingestion, integration, transformation, quality, monitoring, and platform operations. The role balances technical execution with strategic platform planning and helps advance DAFgiving360’s Foundation360 strategy by reducing data complexity, improving data accessibility, increasing platform reliability, and establishing the trusted data foundation required for future AI and automation capabilities.

What You’ll Do

You are a hands-on builder who can move between implementation details and platform-level thinking. You are energized by improving reliability, simplifying complexity, and partnering across teams to deliver data products people trust and use.

Data Platform Engineering

• Build and maintain scalable ingestion, transformation, and integration pipelines.
• Develop reusable data products and shared engineering patterns.
• Improve platform reliability, performance, and maintainability through monitoring, alerting, and operational improvements.
• Support production operations, release activities, and business continuity planning as part of a shared team model.

Architecture & Modernization

• Partner with architects and engineers to implement modern data architecture and engineering standards.
• Onboard new data sources across raw, staging, and analytics-ready layers.
• Simplify legacy data structures, reduce duplicated logic, and align models to business concepts.

Data Quality, Trust, & Governance

• Implement data quality checks, automated testing, and observability practices.
• Partner with Data Governance on metadata, lineage, glossary, and stewardship standards.
• Ensure data solutions align with security, privacy, retention, and compliance requirements.
• Help identify and prevent recurring data defects across critical assets.

Cross-Functional Delivery & Influence

• Translate business and analytics needs into robust technical solutions.
• Recommend improvements that reduce manual effort and improve data accessibility.
• Contribute to technical standards, documentation, and engineering best practices.
• Communicate clearly with technical and non-technical partners and influence direction through strong collaboration.

AI & Future-State Enablement

• Build foundational data assets that support advanced analytics, automation, machine learning, and future AI use cases.
• Improve consistency, usability, trust, and performance of data products that enable faster and more reliable decision-making.
• Contribute to the long-term evolution of DAFgiving360’s data infrastructure so it can scale with digital, analytics, operational, and AI-enabled capabilities.

Tools, Frameworks, & Applications

• BigQuery (Advanced): Data modeling, performance tuning, large-scale ELT, and analytics-ready datasets.
• SQL (Advanced): Complex transformations, optimization, reconciliation logic, and durable semantic layers.
• Python (Advanced): Pipeline development, data processing, validation frameworks, and reusable engineering utilities.
• Google Cloud Platform (Advanced): BigQuery, GCS, IAM-aware data workflows, and cloud-native batch architecture.
• Oracle (Proficient): Source-system extraction patterns, sch