Principal Data Architect - AI

Principal Data Architect About the Role Reporting to the Vice President of Data, the Principal Data Architect is the senior technical authority for how data is modeled, integrated, governed, and consumed across MeridianLink. You will design our enterprise data architecture end-to-end — from source-system ingestion through our Azure Databricks lakehouse and into the analytical, operational, and customer-facing data products our business depends on. This is a hands-on role. You will not only define the meta-models, conceptual models, logical models, and physical schemas that govern our data — you will build them, prove them out in code, partner closely with data engineers as they implement them, and evolve them as the business grows. The right candidate has done this before in a FinTech SaaS environment and understands the trade-offs that come with multi-tenant data, regulated workloads, and customer-facing analytics. What You Will Do • Define the enterprise data architecture: Own the conceptual, logical, and physical data models for MeridianLink's analytical and operational data platform, including source-aligned, integrated, and consumption-ready layers. • Build the meta-model: Design and maintain a meta-model that captures entities, relationships, business definitions, ownership, lineage, sensitivity classifications, and SLAs — and make sure it is wired into our tooling, [... source excerpt omitted ...] eling, Data Vault, and other techniques where they best fit the use case. • Partner across the business: Work closely with Product, Engineering, Analytics, ML, Finance, Risk, and Customer-facing teams to translate business needs into durable data designs. • Influence the roadmap: Identify gaps in tooling, capability, and skill; propose investments; and drive multi-quarter initiatives that materially improve how MeridianLink uses its data. Required Qualifications • 12–15+ years of progressive experience in data engineering, data warehousing, and data architecture roles, with at least the most recent several years at the architect level. • Demonstrated experience as a Data Arc [... source excerpt omitted ...] y related domain). • Deep, hands-on expertise with Databricks and PySpark on Azure, including Delta Lake, Unity Catalog, structured streaming, and performance tuning at scale. • Production experience with Informatica Data Management Cloud (IDMC) — or comparable enterprise integration platforms — for ingestion, transformation, and metadata-driven pipelines. • Proven track record of designing and implementing detailed meta-models and end-to-end data models (conceptual, logical, and physical) that have shipped to production and stood up over time. • Strong command of dimensional modeling (Kimball), Data Vault 2.0, and modern lakehouse patterns, including the ability to choose the