Has knowledge of recent advances in distributed systems (e.g. MapReduce, MPP architectures, and NoSQL databases). You are proficient in a broad range of data design approaches and know when it is appropriate to use them (and when it is not).
Knowledge of engineering and operational excellence best practices. Can make enhancements that improve data processes (e.g., data auditing solutions, management of manually maintained tables, automating, ad-hoc or manual operation steps).
Works with engineers to develop efficient data querying and modeling infrastructure.
Understands how to make appropriate data trade-offs. Can balance customer requirements with technology requirements. Knows when to re-use code. Is judicious about introducing dependencies.
Writing code that a Data Engineer or Software Development Engineer unfamiliar with the system can understand.
Can create coherent Logical Data Models that drive physical design.
Delivers pragmatic solutions. You do things with the proper level of complexity the first time (or at least minimize incidental complexity).
Understands how to be efficient with resource usage (e.g., system hardware, data storage, query optimization, AWS infrastructure etc.)
Collaboration with colleagues from multidisciplinary science, engineering and business backgrounds.
Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions