Lead onshore technical engineers Act as the lead engineer for each domain  Collaborate with Solution Architects to ensure understanding needed for engineering level guidance to nearshore teams  Translate architecture design to engineering level guidance  Own specifying architectural designs into sprint-ready engineering tasks for assigned domain  Ensure delivery to the solution architecture within domain  Answer design questions for sprint teams  Participate in backlog refinement sessions to align team with the architecture  Act as the internal technical expert supporting Product design and Engineering teams on demonstrations and technical discussions.  Create/Update documentation such as solution design patterns and engineering standards.  Lead technical discussions around APIs, integrations, data flow, compliance and security.  Serve as a trusted advisor on best practices, technical implementation, and scalability.  Design and implement highly performant and scalable ETL/ELT data pipelines using Databricks (Spark) and orchestration tools. Develop data ingestion, transformation, and enrichment workflows using Scala, Python, and advanced SQL. Support real-time and batch processing needs with optimized workflows, adhering to best practices for scalability and maintainability. Leverage Databricks for developing and managing Spark-based data pipelines and analytics workloads. Implement Unity Catalog for centralized data governance, access control, lineage, and auditability. Manage Delta Lake schemas and optimize data lake performance using Z-ordering, compaction, and partitioning strategies. Knowledge about testing and validation frameworks into pipelines using tools such as Great Expectations or custom frameworks. Monitor data flows for anomalies, failures, and integrity issues and proactively troubleshoot and resolve them. Knowledge about automated deployment pipelines for data workflows using Azure DevOps, GitHub Actions, or similar. Apply version control and collaborative development practices for notebooks, pipelines, and data models. Partner with Data Scientists, BI teams, and Product stakeholders to understand data needs and translate them into scalable solutions. Mentor junior engineers and help standardize practices across the data engineering team. 
