Senior Data Architect

AmeriLife•Washington, NV

3d•$165,000 - $170,000

About The Position

The Senior Data Architect is a senior technical leader responsible for defining, designing, and governing AmeriLife’s enterprise data architecture across modern cloud platforms. This role leads the design of scalable, secure, and high-performing data platforms, enterprise data models, semantic models, and data products that enable analytics, AI, governance, regulatory reporting, and enterprise decision making. The ideal candidate is a highly hands-on architect with deep expertise in Databricks, enterprise data modeling, advanced SQL, cloud data platforms, Data Vault 2.0, metadata management, and enterprise architecture. This individual partners closely with engineering, analytics, governance, and business teams while actively designing and implementing enterprise-scale data solutions.

Requirements

Bachelor's degree in computer science, Information Systems, Engineering, or a related field. Master’s degree preferred.
10+ years of experience in enterprise data architecture and enterprise data modeling.
5+ years of experience designing cloud-native data platforms and modern data architectures.
Expert-level SQL skills, including query optimization, execution plan analysis, indexing strategies, window functions, stored procedures, complex joins, and performance tuning.
Strong hands-on experience with the Databricks Lakehouse Platform.
Deep expertise in conceptual, logical, physical, dimensional, semantic, and Data Vault 2.0 data modeling.
Extensive experience with enterprise data modeling tools such as Erwin Data Modeler, ER/Studio, Lucidchart, Visio, or equivalent platforms.
Strong experience with Apache Spark, Delta Lake, Unity Catalog, Python, and SQL.
Experience designing enterprise data warehouses, lakehouses, semantic layers, and enterprise data products.
Strong knowledge of Azure cloud services including Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID, Azure Functions, and related services.
Experience implementing CI/CD, Infrastructure as Code, Git, and DevOps best practices.
Strong understanding of metadata management, lineage, governance, master data management (MDM), reference data management, and data quality.
Excellent analytical, communication, and stakeholder management skills.

Nice To Haves

Microsoft Azure Solutions Architect Expert or equivalent certifications.
Databricks Certified Data Engineer Professional or equivalent certification.
Experience implementing enterprise metadata repositories and data catalogs.
Experience with Snowflake Secure Data Sharing.
Experience supporting AI, machine learning, and enterprise analytics platforms.
Experience within financial services, insurance, or other highly regulated industries.

Responsibilities

Lead the architecture and design of enterprise data platforms, enterprise data products, and modern lakehouse solutions.
Define and maintain enterprise conceptual, logical, and physical data models across business domains.
Design canonical data models, semantic models, dimensional models, Data Vault 2.0 models, and normalized data models to support operational and analytical workloads.
Establish enterprise data modeling standards, naming conventions, modeling best practices, and architecture governance.
Design scalable data solutions using Databricks Lakehouse, Delta Lake, Unity Catalog, and modern cloud technologies.
Develop and optimize complex SQL solutions for large-scale analytical workloads, data transformations, data quality validation, and performance optimization.
Partner with Data Engineering teams to ensure architecture standards and enterprise data models are consistently implemented.
Lead architecture reviews, solution design sessions, and technical governance across enterprise initiatives.
Design metadata-driven frameworks, reusable architecture patterns, and standardized data pipelines.
Partner with Data Governance teams on metadata, lineage, stewardship, business glossary, master data management (MDM), and data quality.
Optimize platform scalability, performance, reliability, security, and cost efficiency.
Mentor architects and engineers on enterprise architecture, data modeling, SQL optimization, and engineering best practices.
Evaluate emerging technologies and recommend future-state architecture aligned with enterprise strategy.