Principal Member of Technical Staff-bay Area

Oracle Oracle · Enterprise · Redwood City, CA +1

Design, develop, and debug software for databases and cloud services, focusing on SQL extensions and implementing data structures/algorithms to accelerate query performance. Apply Machine Learning algorithms to automate query processing tasks like result caching, partitioning, and clustering. Analyze and improve feature performance and scalability, contributing to complex, research-centric work in efficient SQL query execution. Provide technical direction and mentorship.

What you'd actually do

  1. Design, develop, troubleshoot and debug software programs for databases and cloud services with emphasis on new extensions to SQL.
  2. Implement data structures and algorithms to accelerate query performance.
  3. Work on Machine Learning algorithms that improve automation of query processing like automated result cache, automated partitioning, automated clustering and zonemaps.
  4. As a member of the software engineering division, you will take an active role in the definition, design, implementation, and evangelization of new database execution features.
  5. You will also be analyzing the performance and scalability of the features and improve them.

Skills

Required

  • BS, MS or PhD degree in Computer Science, or related field
  • Data Structure, Algorithms, Systems Programming, Computer Architecture and SQL
  • C programming and debugging skills
  • Knowledge of parallel processing, thread programming, concurrency control and scalability
  • Strong communication skills, both written and oral
  • Strong personal initiative (go-getter and proactive) and critical reasoning
  • Background in database internals

Nice to have

  • Knowledge of database systems & SQL query execution: joins, aggregation, transactions, re-partitioning and fragmentation
  • Knowledge of database systems theory including SQL query optimization (join ordering, query transformations)
  • Knowledge of internal components of a database system like buffer cache, heap manager, B-Tree indexes, Columnar storage, latches & locks
  • Knowledge of operating systems: concurrency control, multi-threading, inter-process communication

What the JD emphasized

  • new extensions to SQL
  • Machine Learning algorithms
  • automated result cache
  • automated partitioning
  • automated clustering
  • complex and research-centric
  • efficient execution of sql queries
  • vectorized and columnar processing
  • data that is closest match to query vector
  • optimal query plans

Other signals

  • Machine Learning algorithms that improve automation of query processing
  • Work is complex and research-centric
  • leading individual contributor
  • providing direction and mentorship