You will design and build the enterprise data architecture that enables analytics, operations, and customer-facing data products for the organization. You will partner with product, engineering, analytics, ML, finance, risk, and customer-facing teams to translate needs into durable data designs. This is a hands-on, remote role where you will both define standards and implement critical models and reference implementations.

• Define the enterprise data architecture across conceptual, logical, and physical models for analytical and operational platforms.
• Design and maintain a meta-model capturing entities, relationships, business definitions, ownership, lineage, sensitivity classifications, and SLAs; integrate it with tooling.
• Architect and drive lakehouse strategy (medallion bronze/silver/gold Delta Lake patterns), including partitioning, clustering, schema evolution, SCDs, and reproducibility.
• Write PySpark, SQL, and Delta Lake code; build reference implementations, prototype patterns, review pull requests, and personally model critical domains.
• Set patterns for ingestion via enterprise integration platforms and Databricks pipelines, including CDC, batch, streaming, and API-based sourcing.
• Partner with governance, security, and compliance to operationalize cataloging, lineage, classification, masking, and access controls (e.g., Unity Catalog, IDMC).
• Establish and standardize data modeling practices, naming conventions, and review processes; coach engineers on dimensional modeling and Data Vault approaches.
• Identify tooling and capability gaps, propose investments, and drive multi-quarter initiatives to improve how the organization uses data.

• 12–15+ years of progressive experience in data engineering, data warehousing, and data architecture, with several recent years at the architect level.
• Demonstrated experience as a Data Architect at a SaaS company in FinTech or financial services software.
• 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.
• Proven track record designing and shipping meta-models and end-to-end data models (conceptual, logical, physical) to production.
• Strong command of dimensional modeling (Kimball), Data Vault 2.0, and modern lakehouse patterns; ability to choose the right approach per use case.
• Expert SQL skills and strong proficiency in Python/PySpark; comfortable writing production code.
• Experience implementing data governance, lineage, and metadata management programs.
• Working knowledge of FinTech-relevant regulatory and compliance considerations (e.g., GLBA, SOC 2, PCI, NIST, state lending regulations).
• Excellent written and verbal communication skills; able to explain complex data concepts to engineers, executives, customers, and auditors.

• Prior experience designing data architectures for multi-tenant SaaS platforms with customer-facing analytics or embedded reporting.
• Experience supporting loan origination, deposit account opening, or other consumer lending workflows and related data domains (applicants, applications, decisions, funding, servicing, credit data, fraud, KYC/AML).
• Experience building feature stores or curated data products serving ML/AI workloads and BI consumers.
• Familiarity with Azure data services (ADLS Gen2, Azure Data Factory, Event Hubs, Synapse, Purview) in conjunction with Databricks.
• Experience with dbt, Great Expectations, or other modern data quality and transformation tooling on Databricks.
• Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or related field, or equivalent experience.

$126K to 214K/year

This role may fill quickly. Submit your resume to be considered.

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