Data Architect

Brandt is seeking a highly skilled and motivated Data Engineer to join our dynamic team. The ideal candidate will have a strong background in designing, implementing, and optimizing data solutions using Power

BI, Azure, and AI technologies. As a Data Engineer, you will play a crucial role in developing and maintaining efficient data pipelines, ensuring data accuracy, and contributing to business intelligence initiatives.

Duties and Responsibilities

Data Architecture & Strategy

• Data Solution Design:
  Lead the architectural design, data modeling, and implementation of highly scalable data processing systems within the Azure Data ecosystem.
• Strategic Advisement:
  Partner with senior management and internal stakeholders to define data strategies, establish benchmarks, and translate complex business requirements into robust technical specifications.
• Enterprise Capability Advancement:
  Drive the maturity of our enterprise data stack, defining best practices for data organization, governance, and architecture.

Data Engineering & Optimization

• Advanced SQL & Performance Tuning:
  Act as the team's primary SQL authority. Proactively identify, troubleshoot, and optimize inefficient queries, fine-tune execution plans, and ensure optimal database performance.
• ETL/ELT Pipeline Development:
  Architect and maintain robust, scalable data pipelines to extract, transform, and load data from disparate sources into Azure Data Factory and Azure Data Lake Storage.
• Workflow Streamlining:
  Continuously monitor and optimize data workflows to reduce processing times, minimize compute costs, and improve overall system efficiency.

Business Intelligence & Security

• Data Security & Governance:
  Implement strict data security measures, including encryption, robust access controls, and data masking to safeguard sensitive enterprise information.
• Power
  
  BI & Analytics Integration:
  Architect the data models that feed Power
  
  BI, enabling the creation of interactive, high-performance dashboards that provide actionable insights to stakeholders.

#LI-ONSITE

• Deep SQL Mastery: Expert-level proficiency in SQL (T-SQL preferred). Must have hands-on experience with query optimization, execution plan analysis, indexing strategies, complex joins, window functions, and CTEs.
• Data Modeling: Strong background in relational and dimensional data modeling (Kimball/Inmon methodologies), data warehousing, and data dependencies across enterprise systems.
• Azure Ecosystem: Deep expertise in Azure Data Factory, Azure SQL Database, and Azure Data Lake Storage.
• Programming & BI: Highly proficient in Python and Power
  
  BI (including DAX and Power Query optimization).
• Emerging Tech: Familiarity with integrating machine learning models and AI tools into existing data workflows is a strong asset.

• 5–10 years of proven experience in Data Architecture, with a heavy emphasis on Microsoft Azure technologies.
• Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent enterprise experience).
• Demonstrated ability to break down complex, ambiguous processes and explain highly technical concepts to non-technical senior leadership.

• Deep SQL Mastery: Expert-level proficiency in SQL (T-SQL preferred). Must have hands-on experience with query optimization, execution plan analysis, indexing strategies, complex joins, window functions, and CTEs.
• Data Modeling: Strong background in relational and dimensional data modeling (Kimball/Inmon methodologies), data warehousing, and data dependencies across enterprise systems.
• Azure Ecosystem: Deep expertise in Azure Data Factory, Azure SQL Database, and Azure Data Lake Storage.
• Programming & BI: Highly proficient in Python and Power
  
  BI (including DAX and Power Query optimization).
• Emerging Tech: Familiarity with integrating machine learning models and AI tools into existing data workflows is a strong asset.