Principal Data Architect

Team summary:

The Engineering Architecture team sets the technical direction for the Product & Engineering ecosystem at PointClickCare. Their job is to ensure systems are coherent, scalable, secure, and aligned to business strategy.  The team designs and maintains the overall system architecture, establishes shared principals and standards so teams don’t have to reinvent the wheel and translates business priorities into technical direction.

Job Summary:

This role is accountable for making data coherent, scalable, financially sustainable, and architecturally future-proof — ensuring our AI and analytics ambitions rest on a durable foundation.  This role is not a technical lead role buried in implementation. It is a strategic, cross-cutting position responsible for defining and driving a unified data direction across the organization — spanning architecture standards, persistence strategy, technology evaluation, cost optimization, and AI enablement.

Key responsibilities:

• Establish Product & Engineering-wide standards for schemas, naming conventions, event patterns, canonical models, and data representation.

• Define how data persists across multiple layers (OLTP, OLAP, lakehouse, vector stores, feature stores) based on scale, performance, access patterns, governance, and compliance requirements.

• Set best practices for partitioning, sharding, indexing, storage tiering, lifecycle management, and performance optimization.

• Drive reduction of storage and compute costs through architectural optimization, compression, deduplication, and workload rationalization.

• Lead technology evaluations and define decision frameworks (e.g., Databricks vs Snowflake, Hudi vs Delta vs Iceberg, lakehouse vs warehouse, batch vs streaming).

Responsibilities Continued:

• Define when to use batch, micro-batch, streaming, and event-driven architectures across domains.
• Architect data foundations that power AI/ML and GenAI use cases, including semantic layers, feature stores, and vector-based retrieval (RAG).
• Establish governance guardrails for PHI/PII, interoperability standards (FHIR, USCDI), and data contracts.
• Bridge Product and Engineering toward a common data language, replacing fragmented local conventions with standards.
• Drive architectural decisions when teams are misaligned; balance long-term integrity with near-term delivery pressure.
• Protect PCC's ability to own, leverage, and commercialize its data assets while meeting interoperability obligations.

Qualifications:

-10+ years of experience in data architecture, with at least 5 years operating in complex, multi-domain environments

-Deep expertise in multi-layer persistence architectures and modern data platforms (e.g., Snowflake, Databricks, Delta, Hudi, Iceberg).

-Strong understanding of partitioning, sharding, storage tiering, and workload isolation strategies

-Experience enabling production AI/ML workloads, including semantic/feature layers and RAG pipelines.

-Hands-on fluency with tables, events, APIs, and how data structures connect to business models and monetization.

-Experience evaluating and selecting technologies based on cost, performance, scalability, governance, and operational complexity

-Crisp communication skills across technical and executive audiences.

-Understanding of healthcare data standards (FHIR, USCDI) and regulatory frameworks (HIPAA), or ability to ramp quickly.

-Ability to combine strategic vision with pragmatic urgency — knowing when 'good enough now' beats 'perfect later' without allowing architectural drift.

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