Data Architect - R01565081

BrillioAshburn, VA
Onsite

About The Position

We are seeking a client-facing Data Architect to lead enterprise data architecture, cloud modernization, and analytics transformation initiatives. This role combines strategic consulting, stakeholder engagement, and hands-on technical leadership to design scalable cloud-based data and BI solutions using Snowflake, Azure, dbt, Power BI, SQL, Dagster, Fivetran, and Python.

Requirements

  • Bachelor’s degree in Computer Science, Engineering, Analytics, or related field.
  • 7+ years of experience in Data Architecture, Cloud Data Engineering, BI, or Analytics.
  • Strong client-facing consulting and stakeholder management experience.
  • Expertise in Snowflake, dbt, Power BI, SQL, Azure, Dagster, Fivetran, and Python.
  • Experience designing cloud-native data warehouse/lakehouse architectures.
  • Strong knowledge of ETL/ELT, data governance, CI/CD, DevOps, and performance optimization.
  • Excellent communication and executive presentation skills.

Nice To Haves

  • Snowflake, Azure, or Power BI certifications.
  • Experience with Microsoft Fabric, Azure Synapse, and Dynamics 365 F&O.
  • Industry experience in CPG, Manufacturing, Retail, Supply Chain, or Food Manufacturing.
  • Exposure to advanced analytics, forecasting, or machine learning concepts.

Responsibilities

  • Design and implement enterprise cloud data architectures, ETL/ELT pipelines, and modern analytics platforms.
  • Lead data warehouse/lakehouse modernization initiatives using Snowflake and Azure.
  • Develop scalable BI, reporting, and semantic data models with Power BI and SQL.
  • Establish architecture standards, governance, CI/CD, security, and best practices.
  • Partner with business and executive stakeholders to translate requirements into scalable solutions.
  • Lead workshops, roadmap discussions, and architecture governance activities.
  • Optimize data pipelines, orchestration frameworks, and platform performance.
  • Mentor engineering and analytics teams on cloud and data engineering best practices.
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