Azure Data Analytics Engineer

The Azure Data Analytic Enginee r will be the AZURE SME tasked with the development and optimization of cloud-based Business Intelligence solutions. Advances data analytics capabilities and drives innovative solutions. Possesses deep technical expertise in data engineering and plays instrumental role in managing data integrations from on-premises Oracle systems, Cloud CRM (Dynamics), and telematics. Collaborates closely with Data Science and Enterprise Data Warehouse teams and business stakeholders. Primary Responsibilities: Data Ingestion and Storage: Designs, develops, and maintains scalable, efficient data pipelines using Data Factory, and Databricks, leveraging Py Spark for complex data transformations and large-scale processing. Builds and manages extract, transform, and load (ETL)/extract, load, transform (ELT) processes to seamlessly extract, transform, and load data from on-premises Oracle systems, customer relationship management (CRM) technology, and connected vehicles into data storage solutions, such as Data Lake Storage and SQL Database. Integrates and harmonizes data from diverse sources including on-premises databases, cloud services, application programming interfaces (APIs), and connected vehicle telematics. Ensures consistent data quality, accuracy, and reliability across all integrated data sources. Data Engineering: Creates high-code data engineering solutions using Databricks to clean, transform, and prepare data for in-depth analysis. Develops and manages data models, schemas, and data warehouses, utilizing Lakehouse Architecture to enhance advanced analytics and business intelligence. Leverages Unity Catalog to ensure unified data governance and management across the enterprise's data assets. Optimizes data storage, retrieval strategies, and query performance to drive scalability and efficiency in all data operations. GitHub Development: Utilizes GitHub for version control and collaborative development, implementing best practices for code management, testing, and deployment. Develops workflows for continuous integration (CI) and continuous deployment (CD), ensuring efficient delivery and maintenance of data solutions. Technical Expertise: Extensive experience with Data Factory, Databricks, and Synapse, as well as proficiency in Python and PySpark. Data Integration: Experience integrating data from on-premises Oracle systems and connected vehicle data into cloud-based solutions. Lakehouse Architecture & Governance: Deep knowledge of Lakehouse Architecture and Unity Catalog for enterprise data governance. Version Control & Collaboration: Demonstrated proficiency in GitHub for development, collaboration, and deployment in large-scale environments. Infrastructure as Code (IaC): Experience with Infrastructure as Code tools such as Resource Manager (ARM) templates or terraform. Problem-Solving & Troubleshooting: Strong analytical skills with the ability to diagnose and resolve complex data infrastructure challenges. Education/Experience Requirements: BA/BS with 6 to 8 years of relevant experience. Work Environment Hybrid Role: Remote work 2 days per week (After 90 Days Onboarding)