Azure AI & Data Engineer

Ultradent Products-posted 10 days ago
Full-time • Mid Level
Onsite • Salt Lake, UT
1,001-5,000 employees
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The Ultradent Data & Analytics Team is looking for a passionate and solution oriented Azure AI & Data Engineer to help the implementation of data pipelines and supporting data architecture on Azure to increase our analytical solutions that deepen our collective understanding of business activity, influence innovation, and deliver actionable insights. The ideal candidate has outstanding business acumen, a restlessness to answer “why?” questions, excellent analytical abilities, strong technical skills, superior written and verbal communication skills. The Azure ML & Data Engineer is responsible for building, implementing, automating services, and supporting Microsoft BI and AI solutions in the cloud. The candidate will design architectures for data integration and processing to provide high quality datasets and utilize Big Data processing tools to build data and ML pipelines on a modern technology stack. Your area of expertise covers the creation of a complete, end-to-end data pipeline and AI lifecycle: from the import of structured and unstructured raw data, its processing and transformation, metadata management to the implementation of scalable analytics and model deployment platforms. The right individual will possess experience in all stages of database and ML project work (requirements, logical and physical design, implementation, testing and deployment). The Azure AI & Data Engineer should have a firm grasp of modern BI implementation and MLOps methodologies and will have had in-depth experience with the Microsoft BI Stack (SSIS/Data Factory, Azure Data Warehouse, Azure Analytics/SSAS and PowerBi) and Azure AI platforms.

  • Thought leadership in technology innovation and transformation.
  • Maintaining applications ensuring high availability, performance and user experience.
  • Creates and maintains optimal data pipeline architecture on Azure platform.
  • Ensures that system designs adhere to solution architecture design and are traceable to functional as well as non-functional requirements.
  • Designs relational and non-relational data stores on Azure.
  • Builds analytical tools to provide actionable insights from key business performance metrics.
  • Leverages Azure Data Factory and Databricks to assemble large, complex data sets that meet end user and business requirements.
  • Designs new solutions and services to improve overall user experience.
  • Identifies, designs, and implements internal process improvements such as automating manual processes and optimizing data delivery.
  • Defines system design standards to improve and sustain standardization.
  • Expertise in Azure DevOps concepts and implementations, including git, CI/CD pipelines, IaC, docker containers, and Work Items.
  • Assists Manager-Data Administration in leading coordination with other staff to ensure data handling meets organizational objectives for data quality, business process management, and risk management.
  • Provides technical mentoring to other team members for best practices on data engineering and cloud technologies.
  • Designs and Governs Data Models: Defines and enforces logical and physical data models for both the Azure Data Lake and the Azure Synapse Analytics data warehouse.
  • Leads the effort to standardize data definitions, metadata management, and data lineage across the Azure platform to ensure consistency and quality.
  • Architects Scalable Data Ecosystems: Develops and maintains the multi-layered Data Lake architecture (e.g., Raw, Bronze, Silver, Gold zones) to optimize for ingestion, transformation, and consumption of data by both BI and AI initiatives.
  • Designs and implements real-time and batch data ingestion strategies, ensuring low latency and high reliability for critical business data.
  • Evaluates and recommends new Azure services or architectural patterns to continuously improve data security, performance, and cost efficiency of the overall data ecosystem.
  • Ensures Data Quality and Reliability: Establishes and monitors data quality checks and validation rules within data pipelines to maintain a high level of trust in the datasets used for reporting and machine learning models.
  • Implements schema evolution strategies to manage changes in source data and prevent pipeline breaks.
  • Designs and Implements AI/ML Solutions: Develops, trains, and deploys machine learning models using Azure Machine Learning (AML) / Azure AI Studio or equivalent platforms.
  • Builds end-to-end MLOps pipelines (e.g., model registration, automated retraining, deployment via Azure Kubernetes Service/Managed Endpoints) using Azure DevOps and AML.
  • Hands-on experience implementing data and AI workflow automation using n8n, Microsoft Power Automate, or equivalent tools to connect Azure services, databases, and enterprise applications.
  • AI Workflow Integration and Automation: Architects and implements low-code/no-code AI workflow automation using n8n or equivalent tools (e.g., Power Automate, Zapier, Apache Airflow).
  • Integrates AI services (e.g., Azure Cognitive Services, custom models) with business processes, data pipelines, and third-party systems via REST APIs and webhooks.
  • Designs and maintains Real-time AI inference services and ensures their scalability and performance.
  • BA / BS preferred; equivalent work experience will be considered.
  • Minimum of 7 years of successful, results-oriented experience in a diverse range of business and IT functions is ideal.
  • Hands on experience with Azure Data Lake, Azure Data Factory, SQL Data Warehouse Azure Blob, Azure Storage Explorer.
  • Hands on experience with Delta tables and implementing delta logic on data lakes and warehouse.
  • Hands on experience with implementing multi layered data lake architecture.
  • Hands on experience with implementing data warehouse architecture.
  • Hands on experience in creating Data Factory pipelines for on-cloud ETL processing; copy activity, custom Azure development etc.
  • PowerBi experience a plus.
  • Azure PaaS, Data analytics, Data warehousing and Data science.
  • Azure Spark, ADF, T-SQL Scripting, Stored Procs, Python.
  • Azure Synapse Analytics, SQL Pool.
  • Deep knowledge of data ingestion strategies and understanding of the Data Lake and Dimensional data models within Azure.
  • Experience with the Azure storage technologies (Azure Data Lake, Azure SQL Data Warehouse, Azure SQL Database).
  • Experience with Azure data movement and transformation capabilities (Azure Data Factory, Data Lake Analytics, Data Bricks, Stream Analytics).
  • Comfortable with Microsoft SQL data technologies (SSAS/SSIS/SSRS).
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