Azure AI/Data Architect

Requirements

• Experience with streaming architectures: Kafka/Event Hubs, Structured Streaming, CDC
• Knowledge of Power BI/semantic models and cross-platform integrations (Synapse SQL, Fabric, Snowflake)
• Multi-cloud awareness (AWS/GCP) and interoperability patterns
• Financial services or regulated industry experience (e.g., BCBS 239, SOX, HIPAA)
• When you join our team:
• We'll empower you to learn and grow the career you want.
• We'll recognize and support you in a flexible envir

Benefits

Additional Information

We are seeking an Azure-focused AI/Data Architect with deep Databricks experience to design, build, and govern scalable data and AI platforms. This role combines enterprise data architecture, MLOps, and analytics engineering to enable advanced insights and AI solutions across the organization. As a Data Architect in the Global Data Office, you will be responsible for designing, building, and maintaining a reliable and scalable data infrastructure that supports the organization's data collection, storage, transformation, and analysis needs! You will implement robust data orchestration pipelines, along with data gathering, refinement, and processes for improving and validating data to ensure data integrity and accuracy. In this role, you coordinate efforts with business and technology partners to understand and anticipate the necessities for data architecture. You ensure that the data architecture matches the organization's strategic objectives. Your expertise will be vital in crafting an efficient data environment that empowers the organization to bring data-driven insights effectively! Position Responsibilities: Design and implement data integration solutions to facilitate seamless data flow between systems. Architect and oversee data ingestion, transformation, and orchestration using ADF/Synapse pipelines, Databricks Workflows, and event-based patterns Establish data modeling standards (dimensional/Kimball, Data Vault, lakehouse models) and semantic layers for BI/ML consumption Implement governance: data catalogs/lineage, DQ frameworks, security, RBAC/ABAC, and compliance alignment (e.g., Purview, Unity Catalog) Design scalable ML platforms: feature stores, model training/serving, experiment tracking (MLflow), and CI/CD for ML (MLOps) Optimize performance and cost: cluster sizing, autoscaling, storage layout (partitioning/Z-ordering), caching, and query tuning (Spark SQL/PySpark) Define data contracts/APIs and integration patterns for cross-domain interoperability and application consumption Partner with engineering, data science, and business collaborators to translate requirements into technical roadmaps and reference architectures Lead POCs, platform modernization, and migration efforts (on-prem to Azure, batch to streaming, BI to lakehouse) Build standards, templates, and guidelines; mentor teams and conduct architecture reviews Maintain existing integration frameworks and guide each squad Evaluate emerging data technologies and tools to improve data architecture and processes. Monitor and optimize the performance of data systems to ensure efficient data processing Develops, builds, and maintains reliable, efficient and expandable data systems for data collection, storage, transformation, and analysis. Implements data orchestration pipelines, data sourcing, cleansing, augmentation, and quality control processes. Collaborates alongside business and technology partners to gather a clear understanding of current and future data infrastructure requirements. Required Qualifications: 7+ years in data architecture/engineering; 3+ years hands-on with Databricks and Delta Lake Strong Azure expertise: Azure Databricks, Data Factory, Synapse, Event Hubs, Storage (ADLS Gen2), Key Vault, Azure DevOps Proficiency in Spark (Core/SQL), PySpark, and SQL; experience with performance tuning and large-scale data processing Experience designing Medallion Architecture and implementing data governance (Purview/Unity Catalog, DQ frameworks) MLOps/AI platform knowledge: MLflow, feature stores, model lifecycle, and deployment patterns Understanding of security, compliance, and data privacy; implementing least-privilege, encryption, and auditability CI/CD for data and ML, Infrastructure-as-Code (Bicep/Terraform), and automation Good communication, managing relationships, and technical leadership skills Bachelor's Degree: Usually in computer science, information technology, information systems, or a related field. Advanced Degrees: A Master's degree in data science, computer science, or business administration can be advantageous and preferred for senior roles. Several years (typically 5-10) of experience in data management, database design, data warehousing, or a similar field. Experience with ETL tools and processes (e.g., Databricks, Informatica). Knowledge of data integration patterns and standard methodologies. Hands on experiences with technologies like Spark, Hive, Python, Terraform . Experience with cloud platforms such as Azure and AWS.

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