Senior Data Platform Engineer
Posted
Summary
Senior engineer on National Philanthropic Trust's Data Platform team turning governed data into reusable products: Snowflake + dbt modeling, secure pipelines and partner-facing APIs, Infrastructure-as-Code/CI-CD, Power BI support, observability, and AI-ready governance. Hybrid role: onsite Tue-Thu in Conshohocken, PA.
Senior Data Platform Engineer
Become a Part of the NPT Team
National Philanthropic Trust was founded in 1996. Since that time, have raised more than $95.8 billion in charitable contributions and currently manage $68.7 billion in charitable assets. We have made more than 1.1 million grants totaling more than $55.5 billion to charities all over the world. We rank among the largest grantmaking institutions in the United States.
Our mission is to increase philanthropy in society. To that end, our experienced staff of philanthropic professionals are fully prepared to help you establish and administer your donor-advised fund. NPT is led by a Board of Trustees composed of nationally known experts in philanthropy and business.
At National Philanthropic Trust, we foster a welcoming environment for all. People are valued and respected for who they are—with opportunities to bring their entrepreneurial spirit and talents to increase giving around the world. We have an inclusive, supportive, collaborative culture that makes National Philanthropic Trust one of the most rewarding places to work.
What You'll Do
The Senior Data Platform Engineer is a hands-on engineer on NPT's Data Platform team who helps turn governed data into reusable products that support self-service analytics, partner and enterprise reporting, external data delivery, and governed AI-enabled experiences. Working across Snowflake, dbt, Infrastructure-as-Code, and APIs, this person helps NPT define data once and deliver it consistently.
The role drives data products from discovery through production, partnering with Product and business teams and ensuring each product is secure, observable, documented, and supportable. The ideal candidate combines an analytics engineer's modeling discipline, a platform engineer's focus on reliability, and a product mindset.
Data Modeling & Transformation
- Design reusable, domain-focused data products in Snowflake and dbt, including dimensional models with defined grain, conformed dimensions, and shared entities that support reporting, self-service, external delivery, and AI.
- Define governed business rules and trusted financial metrics once, including time logic and point-in-time history, and drive ownership and certification with business domain owners.
- Build, test, document, and optimize dbt models across staging, core, and consumption layers, with business-focused tests and contracts, and maintain modeling standards and lineage.
Data Engineering, Platform Operations & Observability
- Build and operate secure pipelines into and out of Snowflake across APIs, files, Fivetran, data shares, and partner feeds, designed for idempotency, retry, replay, and predictable recovery.
- Develop, deploy, and support partner-facing APIs, secure data shares, and file-based data feeds with defined delivery expectations, monitoring, and reconciliation.
- Manage Snowflake infrastructure, roles, secure views, and masking policies through Infrastructure-as Code, CI/CD, and automated promotion controls.
- Drive platform reliability through service objectives, monitoring and alerting, runbooks, and incident response, while improving lineage, metadata, and cost visibility.
Business Intelligence Development
- Support governed Power BI semantic models and reports on Snowflake consumption products, keeping business logic close to the data.
- Partner with business users on certified data products, self-service, and adoption.
- Support migration of high-value reports and legacy SQL Server, SSIS, and DAX logic to Snowflake with parallel-run reconciliation and controlled cutover.
Technical Collaboration & Governance
- Work with Product and business stakeholders from discovery through adoption to turn business questions into agreed definitions, reusable data products, and measurable outcomes.
- Define and manage data and consumer contracts, service expectations, and entitlement rules for internal and partner-facing products.
- Serve as NPT's technical counterpart to external implementation partners and absorb knowledge transfer to sustain the platform.
- Shape NPT standards for data products, metrics, and platform practices through design and code review, decision records, and runbooks, and mentor engineers and co-op students.
Agentic AI Enablement
- Enable AI by making the platform AI-ready: governed data products, rich metadata, semantic definitions, lineage, and least-privilege access so AI consumes trusted interfaces rather than raw data.
- Support governed AI connectors, Model Context Protocol (MCP) servers, and natural-language experiences with explicit read/write boundaries, accuracy and access evaluation, and human approval for consequential actions.
- Apply NPT AI governance to all AI use: use only approved agentic tools, register tools and use cases, protect sensitive data, and verify and be able to explain every output before it is used or committed.
What You Bring
Education/Certification:
- Bachelor's degree in Computer Science, Information Technology, Data Science, Data Engineering, or a related field, or equivalent practical experience.
- Relevant certifications are a plus, such as SnowPro, dbt Certified Developer, or an infrastructure-as-code certification.
Experience:
- 7+ years of experience in data engineering, analytics engineering, or data warehousing, including hands-on experience driving production dbt models and dimensional designs.
- Experience building, consuming, and supporting production APIs and delivering governed data products to multiple consumers (reports, self-service, partner feeds), including access controls.
- Experience modernizing legacy reporting or data pipelines, including reconciliation to systems of record, and working across business, security, and vendor teams.
- Experience working in Agile/Scrum environments, using tools such as Jira and Confluence for backlog management, sprint delivery, and documentation.
Required Technical Skillsets
- Expert SQL, dimensional modeling, and data warehouse design, including grain, conformed dimensions, slowly changing dimensions, and query optimization.
- Production dbt capability across models, tests, macros, incremental strategies, snapshots, documentation, and medallion-layer design.
- Hands-on Snowflake engineering, including role hierarchy and least privilege, secure views, row access policies, masking, data sharing, and tasks.
- Infrastructure as code using tools such as Terraform, schemachange, or comparable, plus Python, Git, and CI/CD platforms such as GitHub Actions or Azure DevOps.
- Hands-on API experience building and consuming REST or comparable APIs, including designing, securing, and supporting APIs that expose data to partners and applications, with OpenAPI, OAuth 2.0, and versioning.
- Data pipeline engineering: batch and incremental pipelines from APIs, files, and SaaS sources, with automated testing, monitoring, and recovery.
- Data security and governance practices, including role-based access, masking, classification, lineage, and metadata management, and responsible use of AI tooling.
Preferred Technical Skillsets
- Power BI semantic modeling and DAX; ability to read legacy T-SQL, SSIS, and Power Query logic.
- Ingestion and operations: Fivetran, Fabric or Azure Data Factory, Airflow or comparable, change data capture, MuleSoft, SFTP, Datadog, and FinOps.
- Partner and semantic tooling: GraphQL, contract testing, dbt Semantic Layer, Streamlit, and catalog and lineage tools such as Purview or Atlan.
- Snowflake Cortex, Microsoft Copilot agents, MCP or comparable agent protocols, AI evaluation, and governed machine learning operationalization with Python; experience in financial services, philanthropy, or Salesforce.
Knowledge, Skills and Abilities:
- Product and platform mindset: starts with consumers and agreed definitions, favors reusable data products and standards over one-off requests, and designs for security, observability, and recovery.
- Strong analytical judgment and accountability, including data accuracy, challenging vague requirements, sound judgment on AI-generated work, and driving the fix to resolution.
- Clear communication across technical, business, and partner audiences, and pragmatic, independent execution in a lean team.
Hybrid Work Schedule:
- This position follows a hybrid work schedule and requires onsite presence in our Conshohocken, PA office every Tuesday, Wednesday, and Thursday, with the flexibility to work remotely on Mondays and Fridays.
Physical Requirements:
- Prolonged periods of sitting at a desk and working on a computer.
Skills
- Agentic AI
- Agile
- AI
- Ai Enablement
- Airflow
- Analytics
- API
- Azure
- Azure Data Factory
- Azure DevOps
- Business Intelligence
- CI/CD
- Confluence
- Data Engineering
- Data Modeling
- Data Pipelines
- Data Science
- Data Warehousing
- Datadog
- dbt
- DevOps
- Dimensional Modeling
- FinOps
- Fivetran
- Git
- GitHub
- GitHub Actions
- GraphQL
- Infrastructure as Code
- Jira
- Machine Learning
- MCP
- Metadata Management
- Microsoft Copilot
- Mulesoft
- OAuth
- Observability
- OpenAPI
- Power Query
- Power BI
- Python
- SaaS
- Salesforce
- Scrum
- SFTP
- Snowflake
- SQL
- SQL Server
- SSIS
- Streamlit
- Terraform
- Test Automation