Job Description: Senior Data Engineering Leader We are seeking a highly experienced Senior Data Engineering Leader with 15+ years of experience in big data to take a lead onshore and offshore engineering teams for a major transformation program as a manufacturing leader. This role is 60-70% hands-on development and 30-40% team leadership and stakeholder management. The candidate must have extensive experience and expert skills in the following areas to efficiently lead: \\ • Programming Skills: Must have strong hands-on Spark, Python, PySpark, and SQL expertise. • Big Data and Analytics: Knowledge of big data technologies like Azure Databricks and Synapse. • Cloud Data Engineering concepts: Must demonstrate knowledge of Medallion Architecture and common ETL patterns, including ingestion frameworks. • Performance tuning techniques and best practices: Understanding of performance analysis and system architecture is essential. • Cloud data platform: Preferably MSFT Fabric, Azure Synapse, Azure Databricks, or any other cloud data platform. • Data modeling skills: Strong skills and knowledge of dimensional modeling, semantic modeling, and standard data modeling patterns used in analytical systems. • Data Management and Storage: Proficiency with Azure SQL Database, Azure Data Lake Storage, Azure Cosmos DB, Azure Blob Storage, etc. • Data Integration and ETL: Extensive experience with Azure Data Factory for data integration and ETL processes. • Analytical Skills: Strong analytical and problem-solving skills. • Problem-Solving & Technical Leadership Skills: Ability to identify, design, and implement improvements that drive optimal performance. • Leadership & Collaboration: Experience leading onshore and offshore teams, fostering collaboration, and driving high-performance engineering culture. • Stakeholder Management: Strong analytical and communication skills, with experience working closely with business and technical stakeholders to align on requirements. Responsibilities: • Lead onshore and offshore data engineer team, provide expert guidance and collaborate with business stakeholders. • Design and Build Data Pipelines: Develop and manage modern data pipelines and data streams using PySpark as well as data factories and data pipelines. • Database Management: Develop and maintain databases, data systems, and processing systems. • Data Transformation: Transform complex raw data into actionable business insights using PySpark. • Technical Guidance: Collaborate with stakeholders and teams to assist with data-related technical issues. • Data Architecture: Ensure data architecture supports business requirements and scalability. • Big Data Solutions: Utilize Databricks or Synapse for big data processing and analytics. • Process Improvements: Identify, design, and implement process improvements, such as automating manual processes and optimizing data delivery.