• Conceptualize and own the design, implementation and operations of the next-generation data lake containing complex Genentech data from any Genentech business areas and operating within a global environment

• Conceptualize and own the data architecture for multiple large-scale projects across analytics, automation, data science and artificial intelligence, while evaluating design and operational cost-benefit tradeoffs within systems across different partnered business areas

• Solve challenging data integration problems for complicated and diverse data sets, utilizing and coding optimal ETL patterns, frameworks, query techniques, and sourcing / merging from structured and unstructured data sources

• Implement and optimize data pipelines, data quality metrics, and systems to facilitate easier development of data artifacts and support machine learning systems at scale

• Design and develop MLOps Infrastructure to build, train and deploy ML Models using cloud native services

• Design and develop secure, scalable, high-performance and reliable data and analytics solutions in a cloud platform (preferably AWS)

• Define and manage SLAs for all data sets in designated areas of ownership

• Collaborate with engineers, business managers, and data scientists from different business areas globally to understand complex data needs and provide key data insights in meaningful ways

• Identify and evaluate new technologies to improve performance, maintainability, and reliability of data systems.

Minimum Requirements

Bachelor's degree in Information Systems, Computer Science, Computer Engineering, or related field with additional experience

• 5-8 years of experience as a Data Engineer, or in a similar role

• Experience designing, evolving and running data lakes with complex data and multiple customer groups

• Experience developing data pipelines using data from heterogeneous data sources (eg. AWS, Oracle, SAP, Salesforce, etc.)

• Experience with data modeling, data warehousing, and building data pipelines

• Experience implementing and monitoring data quality metrics in a data governance environment

• Experience with MLOps

• Experience with AWS, AWS Data Engineering and MLOps tools and services (ie: S3, Redshift, Athena, Glue, Lambda, SageMaker etc)

• Experience with software development and version control systems

• Experience designing and building large-scale technology products

• Experience leading engineering discussions around technology decisions

• Ability to understand complex problems and collaboratively develop ad hoc solutions in rapidly changing environment

• Strong technical communication skills