Senior Principal Data Engineer

BS with 10+ years of relevant Industry experience/M.S. in Computer Science with 7+ years of relevant Industry experience.
Strong fundamentals: algorithms, distributed computing, data structure, database
Fluency with at least one of: Go/Java/Python/C++/Scala/SQL
5+ years of industry experience on very large scale analytics or ML systems development
2+ years of experience with Google Cloud Platform (BigQuery, Dataproc, Composer, Dataflow, BigTable, etc.)
2+ years of experience in Hadoop technologies (Map/Reduce, Pig, Hive, HBase, Spark, Kafka, Oozie, etc.)
Experience in data modeling, schema design, ETL, and data analysis
Self-driven, challenge-loving, detail oriented, teamwork spirit, excellent communication skills, ability to multitask and manage expectations

Responsible for designing, implementing and maintaining data pipeline/analytics architectures of Yahoo’s Consumer Monetization Platform
Improve our existing data infrastructures for machine learning and deep learning using your core expertise
Interact with data analysts, data scientists, product managers, and software engineers to understand business problems, technical requirements to deliver data solutions
Work with other engineers to implement algorithms and systems in an efficient way
Take end to end ownership of Machine Learning-based distributed data systems – from data pipelines and training, to real time prediction engines
Develop complex queries, very large volume data pipelines, and analytics applications
Develop complex queries and software programs to solve analytics and data mining problems
Prototype new metrics or data systems
Lead data investigations to troubleshoot data issues that arise along the data pipelines
Maintenance and improvement of released systems
Engineering consulting on large and complex warehouse data

Experience with machine learning algorithms, NLP, and/or statistical methods a big plus
Experience in any of: machine learning, analytics, data mining, or data mart and warehouse
Experience with Deep Learning platforms (Tensorflow/Keras/Spark MLlib)