at Applied Materials in Memphis, Tennessee, United States
At Applied Materials, we are building the next generation fab productivity solutions using Artificial Intelligence and Machine Learning. Our AI/ML team is looking for a Full Stack Engineer who will be responsible for expanding and optimizing APF data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The Full Stack Engineer will support our data scientists and machine learning engineers on data and machine learning initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. The right candidate will be excited by the prospect of optimizing or even re-designing our companys data architecture to support our next generation of products and data initiatives.
This position can be located anywhere in USA.
+ Create and maintain optimal data pipeline architecture
+ Assemble large, complex data sets that meet functional / non-functional business requirements.
+ Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and big data technologies
+ Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
+ Work with stakeholders including the Product management, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
+ Create data tools for analytics, data scientist and machine learning team members that assist them in building and optimizing our product into an innovative industry leader.
+ Work with data and analytics experts to strive for greater functionality in our data systems.
+ Advanced working knowledge of object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
+ Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
+ Experience building and optimizing big data data pipelines, architectures and data sets.
+ Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
+ Strong analytic skills related to working with unstructured datasets.
+ Build processes supporting data transformation, data structures, metadata, dependency and workload management.
+ A successful history of manipulating, processing and extracting value from large disconnected datasets.
+ Working knowledge of message queuing, stream processing, and highly scalable big data data stores.
+ Strong project management and organizational skills.
+ Experience with machine learning related libraries, such as scikit-learn, pandas, TensorFlow, Keras, etc.
+ Experience supporting and working with cross-functional teams in a dynamic environment.
+ Experience with big data tools: Hadoop, Spark, Kafka, etc.
+ Experience with relational SQL and NoSQL databases, including Postgres, Cassandra, MongoDB, ClickHouse
+ Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
+ Experience with cloud services: such as EC2, EMR, RDS, Redshift, Azure services
+ Experience with stream-processing systems: Storm, Spark-Streaming, etc.
+ Candidate with 5+ years of experience in a Full Stack Engineer, Data Engineer, Data Science Developer or Machine Learning Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems, Engineering or another quantitative field.
Years of Experience:
4 – 7 Years
Yes, 10% of the Time
Applied Materials is committed to diversity in its workforce including Equal Employment Opportunity for Minorities, Females, Protected Veterans and Individuals with Disabilities.To view full details and how to apply, please login or create a Job Seeker account