Data Engineer

The Applications Development Technology Lead Analyst is a senior level position responsible for establishing and implementing new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to lead applications systems analysis and programming activities. This is a hands-on technology leadership role responsible for architecting, developing, optimizing, and operating large-scale batch and real-time data-processing platforms at an enterprise scale. The position requires a candidate who can combine deep technical expertise in distributed data processing with effective team leadership, stakeholder management, and end-to-end delivery accountability. The successful candidate will serve as a subject-matter expert in modern data engineering, guiding both technical strategy and execution. The Data Engineering Lead will drive the design and implementation of high-throughput data solutions capable of handling multi-terabyte to petabyte-scale datasets. This role balances advanced development and performance engineering with the leadership of a global team of data engineers, ensuring the delivery of robust, scalable, and maintainable data pipelines.

Partner with multiple management teams to ensure appropriate integration of functions to meet goals as well as identify and define necessary system enhancements to deploy new products and process improvements Resolve variety of high impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards Provide expertise in area and advanced knowledge of applications programming and ensure application design adheres to the overall architecture blueprint Utilize advanced knowledge of system flow and develop standards for coding, testing, debugging, and implementation Develop comprehensive knowledge of how areas of business, such as architecture and infrastructure, integrate to accomplish business goals Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions Serve as advisor or coach to mid-level developers and analysts, allocating work as necessary Lead the architecture, design, and development of high-throughput batch and real-time streaming data pipelines using Apache Spark, Databricks, and Kafka. Serve as the senior subject-matter expert for Spark architecture, job optimization, SQL query tuning, distributed processing, and Databricks performance engineering. Write, review, and approve clean, modular, and highly performant Scala and Python code, enforcing best practices across the team. Diagnose and resolve complex performance bottlenecks by analyzing Spark DAGs, execution plans, and executor metrics to address issues like data skew, shuffling, and memory management. Apply advanced optimization techniques for Spark and SQL, including broadcast joins, predicate pushdown, partition pruning, Z-Ordering, and adaptive query execution. Manage and optimize Databricks workspaces, including Unity Catalog, Delta Live Tables, job clusters, security, governance, and monitoring. Implement and manage Apache Iceberg or Delta Lake as an enterprise table format, applying knowledge of features such as hidden partitioning, schema evolution, and compaction strategies. Develop and enforce enterprise data-modelling standards and Lakehouse practices, including medallion architecture (Bronze, Silver, Gold layers). Lead, mentor, and develop a global team of data engineers, conducting code reviews, architecture reviews, and technical design sessions. Manage technical priorities, delivery milestones, dependencies, and risks, and collaborate effectively with business stakeholders, product owners, architects, and support teams. Define and enforce standards for coding, testing, deployment, monitoring, data quality, operational readiness, and production support. Appropriately assess risk when business decisions are made, demonstrating