Organizational Overview

Smile Train is changing the world one smile at a time. Our goal is to transform the lives of every person affected by a cleft lip or palate. We train and support local doctors and medical professionals to provide beneficiaries with free, life-changing, comprehensive cleft treatment. Our sustainable model has enabled us to help with over 2 million surgeries in 90+ countries, and we’re just getting started. We are truly changing the world with one smile at a time.

Smile Train is looking for exceptional people to join a worldwide team of dedicated, passionate professionals. Our team is comprised of creative and highly motivated individuals working to make a positive impact. Join us!

Reports to: Senior Vice President, Technology, Data, & Innovation

Role Summary

The Data Architect designs and governs Smile Train’s enterprise data architecture and data platform to ensure data is trusted, well-modeled, secure, and ready for analytics and AI. This role supports Smile Train Express (STX), our patient health records platform, by defining canonical models, integration patterns, and semantic layers that enable consistent reporting, self-service analytics, and responsible AI use cases.

Key contributor to Smile Train’s Data & AI future-state architecture, including ELT-first ingestion, tiered Lakehouse storage, governance/security by design, and governed BI/semantic consumption.

Accountability (What You’ll Be Accountable For)

A clear, adopted target-state enterprise data architecture aligned to Smile Train’s Data & AI roadmap and the future state architecture design.

Canonical enterprise data models and shared definitions (business glossary) for core entities, metrics, reference data, and STX-aligned domains.

A governed, secure, and compliant data foundation (role-based access policies, encryption/masking, retention, auditability).

Operationalized metadata, lineage, and documentation so data is discoverable, understandable, and reusable across teams.

Measurable improvements in data quality (accuracy, completeness, timeliness) through defined rules, monitoring, and root-cause remediation.

Durable integration patterns and data contracts between STX and enterprise systems that reduce duplication and support a single source of truth.

Reference architectures and reusable patterns that accelerate delivery and reduce technical debt across the data platform lifecycle.

Key Responsibilities

Architecture & Design

Design end-to-end enterprise data architecture spanning ingestion, storage, processing, semantic layer, and consumption to support analytics and AI.

Translate business requirements into conceptual, logical, and physical data models; define conformed dimensions and subject-area data products.

Define canonical models and interoperability patterns that connect STX and other enterprise systems and enable cross-department reporting.

Ensure solutions align with enterprise architecture principles, roadmaps, and the Data & AI future state architecture (automation-first, tiered storage, governed access).

Standards, Governance & Quality

Establish and maintain data modeling, naming, and metadata standards; partner with governance stakeholders to implement catalog, glossary, lineage, and data quality rules.

Define and enforce security, privacy, and access control standards (PII/PHI handling, masking, encryption, role-based policies).

Create and maintain methods to track data quality, completeness, redundancy, and improvement; support remediation planning and execution.

Create strategies for backup, disaster recovery, business continuity, and archiving as required by platform and compliance needs.

Delivery & Technical Leadership

Provide architectural guidance to engineers on ELT/ETL pipelines, performance optimization, and scalable patterns for batch and (as-needed) streaming.

Create reference architectures and reusable templates (ingestion patterns, lakehouse/medallion patterns, semantic layer conventions, data product blueprints).

Conduct design and architecture reviews; help troubleshoot complex data issues and performance bottlenecks.

Partner with product and program leaders to prioritize technical debt, refactoring, and platform enhancements.

Collaboration & Stakeholder Management

Partner with stakeholders across Programs, Fundraising, Finance, Technology, and Operations to understand information needs and critical KPIs.

Communicate architectural concepts in clear, business-friendly language and drive adoption of standards and shared definitions.

Collaborate with vendors and implementation partners to select and implement tools and services that meet Smile Train goals.

Documentation & Continuous Improvement

Maintain current and accurate documentation of enterprise data models, data flows, lineage, and data contracts.

Evaluate tools and modern practices that improve scalability and speed (e.g., semantic layer enablement, metadata-driven frameworks, domain data products).

Identify opportunities for data reuse, migration, consolidation, and retirement of legacy processes.

Qualifications

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Data Science, Statistics, Engineering, or related field (or equivalent experience).

7+ years of experience in data engineering, data warehousing, BI, or data architecture, with 3+ years in a lead/architect role (preferred).

Proven expertise in data modeling (3NF, dimensional/star schema, semantic models; Data Vault a plus).

Strong SQL skills and experience designing enterprise data platforms for analytics; experience with cloud platforms (AWS/Azure/GCP) preferred.

Experience with metadata management, lineage, governance, and data quality practices; familiarity with catalog/governance tools is a plus.

Strong understanding of security and privacy concepts (encryption, masking, roles, PII/PHI handling) and global privacy requirements.

Excellent communication and stakeholder management skills; able to balance strategy with hands-on problem solving.

Nice to Have

Patient record/EHR platforms or distributed partner ecosystems

AI/ML data design (feature-ready datasets, monitoring/drift)

Semantic layer & metrics governance tools (e.g., Looker, AtScale, dbt, Cube)

Data platform modernization/migrations (including archiving)

Team supervision/management experience

Why You’ll Love Working Here

• Comprehensive health, pharmacy, dental, and vision coverage
• 403(b) retirement plan with employer match
• Generous PTO, holidays, and sick time
• Paid parental leave and family care benefits
• Annual professional development stipend
• Hybrid work flexibility from our Midtown Manhattan office

Application Process

Smile Train is an equal opportunity employer committed to inclusive hiring and dedicated to diversity in our work and staff. To apply, please complete the application online and include your CV and a cover letter explaining why you are a good fit.