Principal Data Architect

At Sparq, we help companies solve the right problems—not just build more technology. We’re a modern product engineering partner blending strategy, craftsmanship, and speed to help organizations modernize confidently in the age of AI. From data ecosystems to digital products and AI acceleration, we turn complexity into clarity and ideas into impact. If you’re driven to build what’s next, lead with empathy, and deliver excellence without ego, you’ll feel right at home at Sparq. **Why you will enjoy Mondays again:** - Opportunity to work alongside a talented, diverse team in a collaborative and creative environment - Ongoing investment in your growth through training, mentorship, and industry certifications - Hands-on exposure to cutting-edge technologies across multiple industries - Recognition programs and employee awards within an inclusive culture that celebrates great work, including our AI Innovators Program - Remote or hybrid work options **A Day in the Life:** - Design and evolve scalable AWS-based data architecture across operational, analytical, lakehouse, pipeline, and semantic layers. - Architect multi-tenant SaaS data models, determining when to use Silo, Pool, or Hybrid isolation strategies based on tenant tiering, contractual obligations, and data security requirements. - Bridge operational and analytical data needs by designing high-throughput OLTP systems alongside scalable OLAP and lakehouse solutions for reporting, AI/ML, and executive analytics. - Actively leverage AI tools to streamline tasks, improve the quality of your work, and share best practices with teammates while continuously seeking new ways to integrate AI into your everyday workflows - Design and optimize data platforms using AWS technologies such as Amazon Aurora, DynamoDB, AWS Glue, S3, Apache Iceberg, Amazon Redshift, and related services. - Build data architecture that supports complex healthcare data structures, including claims data, with a strong focus on privacy, security, compliance, and governance. - Lead technical design discussions, identifying dependencies, risks, tradeoffs, and compliance considerations to support predictable delivery. - Establish best practices for data modeling, pipeline design, taxonomy, data quality, lineage, observability, and platform-wide standards. - Design secure cross-account and multi-region AWS data governance patterns using AWS Organizations, AWS Control Tower, IAM Identity Center/SSO, and AWS Lake Formation. - Support enterprise-grade DataOps practices, including automated data quality checks, end-to-end lineage, observability, and infrastructure-as-code using AWS CDK or Terraform. - Translate architecture strategy into actionable technical plans, including epics, features, and sprint-ready stories for engineering teams. - Serve as a technical bridge across Data Engineering, Data Science, Security, Compliance, Product, and Enterprise Architecture teams. - Mentor engineers, review complex data architecture and code, and elevate system-level thinking across the team. **What it takes:** - Deep expertise designing modern AWS-based data architectures across operational, analytical, lakehouse, pipeline, and semantic layers. - Expert-level experience designing multi-tenant SaaS databases, including Silo, Pool, and Hybrid isolation models. - Proven ability to bridge OLTP and OLAP design patterns across operational systems, lakehouse platforms, and enterprise analytics environments. - Hands-on experience with AWS data and database technologies such as Amazon Aurora, DynamoDB, AWS Glue, S3, Apache Iceberg, Amazon Redshift, and related services. - Experience designing systems that process complex healthcare data structures, with healthcare claims data experience strongly preferred. - Strong understanding of data privacy, security, compliance, and governance requirements in healthcare or other regulated environments. - Advanced experience with cross-account and multi-region AWS governance using AWS Organizations,…