A critical role of Magic Leap's Business Technology (BT) solutions teams, Lead Data Engineering will support data warehouse, data analysis initiatives and ensure optimal data and information delivery architecture to support ongoing projects. The incumbent must be self-directed and comfortable supporting the data engineering and Business intelligence needs of multiple teams, systems and products. You will be reforming the company’s data architecture to support our next generation of products and data initiatives. In addition, this role will evaluate new or existing programs, maintenance, improvement, and support the business application solutions for internal business functions, which are based on the requirements and needs of such a client base.
Responsibilities:
- Create and maintain optimal data pipeline architecture
- Integrate large, complex data sets that meet business requirements
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability
- Evaluate/Review/Implement/Build the infrastructure required for optimal extraction, transformation, and loading of data from various data sources
- Work with stakeholders at multiple levels to assist with data-related technical issues and support their data infrastructure needs
- Support data analysis tools used by multiple business intelligence resources throughout the organization
- Work with data and analytics experts to strive for greater functionality in the data ecosystem
Qualifications:
- Advanced working SQL knowledge and experience working with relational databases and working familiarity with a variety of databases
- Experience implementing and supporting cloud based data analytics tools in a scalable architecture
- Experience building and optimizing big data pipelines, architectures, and data sets
- Experience performing root cause analysis on internal & external data and processes and identify opportunities for improvement.
- Strong analytic skills related to working with both structured and unstructured datasets
- Build processes supporting data transformation, data structures, metadata, governance, and workload management
- Experience supporting and working with cross-functional teams in a dynamic environment