We are seeking a highly skilled and motivated Senior Data Engineer with 8+ years of experience to join our team. This role will be pivotal in designing and building our domain data interface. The ideal candidate will be an individual contributor capable of leading design discussions, making informed design decisions, and collaborating with stakeholders to create canonical data models. This role offers the opportunity to play a crucial part in shaping the architecture and success of our data solutions. Qualifications • Strong expertise in supply chain concepts. • Deep understanding of system design principles and data architecture. • Proven experience with Databricks, data governance, and data engineering best practices. • Excellent coding skills and experience in conducting detailed code reviews. • Strong documentation skills to maintain clear and comprehensive project records. • Ability to work independently and lead initiatives as an individual contributor. • Strong communication skills for effective stakeholder collaboration. Required Technical Skills • 8+ years of experience in data engineering or related fields. • Proficiency in Python, PySpark, and SQL. • Azure, Databricks, and Azure data factory • Experience developing/designing scalable microservices in Java would be a plus. • Experience with data modeling tools and frameworks. • Strong understanding of data engineering concepts and hands-on experience with Databricks. • Familiarity with system design concepts relevant to data architecture. • Knowledge of data governance practices, including the use of Unity Catalog. Key Responsibilities • Design, build, and maintain scalable data pipelines and ETL processes using Azure Data Factory, Databricks, and PySpark. • Architect and implement cloud-based data solutions on Azure, leveraging services such as Azure Synapse Analytics, Azure Data Lake Storage, and Azure SQL. • Develop and optimize complex SQL queries and stored procedures for data transformation and reporting. • Work with structured and unstructured data to create clean, reusable datasets for analytics and business intelligence. • Monitor, troubleshoot, and improve the performance and reliability of data workflows and pipelines. • Ensure data quality, governance, and security standards are consistently met. • Collaborate with data scientists and analysts to support ML and AI initiatives. • Contribute to best practices, documentation, and knowledge sharing within the data engineering team.