Data Architect - Onsite, MD

* *Data Engineering & Pipeline Architecture:* Design, build, and maintain real-time and batch data pipelines using Azure Event Hubs, Azure Data Lake Storage, and Azure Databricks. Harmonize, transform, and curate raw telemetry and operational datasets into consumable views using Spark SQL, Spark DataFrames, and Scala/Python for distributed processing.

* *Machine Learning & Predictive Analytics:* Develop, train, and deploy statistical models, deep learning, and ensemble architectures via Python and Azure Machine Learning. Optimize systems for predictive maintenance, energy efficiency, and operational anomaly detection across facility climate control systems.

* *Software Development & Operations (DevOps):* Manage the full software development lifecycle (SDLC) using Git/GitHub for source control and Jira for requirements tracking. Implement Infrastructure as Code (IaC), CI/CD pipelines, and Azure DevOps for automated model deployment, monitoring, and pipeline orchestration.

* *Business Intelligence & Data Visualization:* Build interactive dashboards and operational monitoring tools using Power BI, Tableau, and Azure Grafana. Translate complex model outputs into actionable insights for strategic decision-making and operational improvements.

* *Data Governance & Security:* Enforce data governance frameworks to maintain high data quality and accuracy. Develop and execute data protection strategies using Azure security services, including identity and access management (IAM), data classification, permissions management, and threat detection.

* *Strategic Collaboration & Technical Advisory:* Serve as a technical advisor on data quality, system scalability, and facility data architecture to ORF leadership. Collaborate with data scientists, subject matter experts, and internal stakeholders to integrate practical operational constraints into analytical solutions and lead technical workshops.

* *Core Experience:* Demonstrated experience combining software engineering practices with data science methodologies across the full software development lifecycle.

* *Big Data & Cloud:* Expertise in distributed computing (Apache Spark, Scala, PySpark), Azure Cloud Architecture (Databricks, Synapse Analytics, Event Hubs, Data Lake Storage), and Infrastructure as Code (IaC).

* *Data Science & ML:* Proficiency in Python, SQL, and advanced machine learning frameworks for end-to-end MLOps (Azure Machine Learning).

* *Visualization & Security:* Hands-on experience with enterprise BI tools (Power BI, Tableau, Azure Grafana) and cloud security frameworks for data governance.