Senior Data Architect

Aureon•West Des Moines, IA

1d•Onsite

About The Position

We are seeking a Senior Data Architect to define and own the enterprise data architecture across analytical, operational, and integration platforms. This role involves designing logical, physical, and conceptual data models to support various use cases including analytics, reporting, AI/ML, and operational needs. The Senior Data Architect will establish and enforce data architecture standards, patterns, and best practices, and design scalable cloud-based and hybrid data platforms such as data lakes, lakehouses, and warehouses. A key aspect of this role is partnering with business, analytics, security, and application teams to translate requirements into data solutions, ensuring alignment with security, governance, privacy, and compliance requirements. The position also entails leading architecture reviews, providing technical guidance to data engineers and analytics teams, and evaluating/recommending data technologies, tools, and platforms. Additionally, the role will oversee data governance, metadata management, data quality, and lineage initiatives. On the data engineering side (approximately 30% of the role), responsibilities include designing and building robust data pipelines (batch and streaming) for ingestion, transformation, and delivery, developing and optimizing ETL/ELT processes using modern data engineering frameworks, and collaborating with engineers to implement architectural patterns in production systems. Ensuring pipelines are reliable, scalable, performant, and cost-efficient, troubleshooting data issues, and optimizing queries, storage, and processing are also critical. Support for CI/CD practices, automated testing, and monitoring for data workflows is expected.

Requirements

Bachelor's degree in computer science, Information Systems, Engineering, or equivalent experience
8+ years of experience in data architecture and/or data engineering roles
Proven experience designing enterprise-scale Azure
Strong hands-on experience with: Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Data Factory
Advanced SQL skills
Strong knowledge of data modeling, performance tuning, and cost optimization in Azure
Experience integrating data from SaaS, on-prem, and cloud-based systems
Ability to clearly communicate architectural decisions to both technical and non-technical stakeholders
Background with Data Governance Framework

Nice To Haves

Experience with Python (preferred)
Azure Databricks or Microsoft Fabric
Experience designing data architectures that support AI and machine learning workloads, including feature engineering, training, inference, and monitoring at scale.
Experience with Microsoft Fabric (OneLake, Lakehouse, Warehouse, Power BI integration)
Familiarity with Power BI semantic models and analytics consumption patterns
Knowledge of Azure Purview / Microsoft Purview for data governance and lineage
Experience with event-driven and streaming architectures in Azure
Understanding of DataOps / DevOps practices in an Azure environment
Experience supporting regulated or security-conscious enterprise environments

Responsibilities

Define and own enterprise data architecture across analytical, operational, and integration platforms
Design logical, physical, and conceptual data models to support analytics, reporting, AI/ML, and operational use cases
Establish and enforce data architecture standards, patterns, and best practices
Design scalable cloud-based and hybrid data platforms (e.g., data lakes, lakehouses, warehouses)
Partner with business, analytics, security, and application teams to translate requirements into data solutions
Ensure data solutions align with security, governance, privacy, and compliance requirements
Lead architecture reviews and provide technical guidance to data engineers and analytics teams
Evaluate and recommend data technologies, tools, and platforms
Ensure data governance, metadata management, data quality, and lineage initiatives
Design and build robust data pipelines (batch and streaming) for ingestion, transformation, and delivery
Develop and optimize ETL/ELT processes using modern data engineering frameworks
Collaborate with engineers to implement architectural patterns in production systems
Ensure pipelines are reliable, scalable, performant, and cost-efficient
Troubleshoot data issues and optimize queries, storage, and processing
Support CI/CD practices, automated testing, and monitoring for data workflows