Amazon Elastic File System (EFS) is built in Boston, MA. It's a fully managed service that makes it easy to set up and scale shared file storage in the AWS Cloud. Amazon EFS is designed for a wide variety of use cases: data analytics, video rendering, genomics analysis, web serving, content management, and home directories, to name a few. https://aws.amazon.com/efs/
As a Data Engineer in AWS EFS you will work on the data pipeline and analytics to provide business and engineering stakeholders key insights into our customers' filesystem performance. You will get the exciting opportunity to interact with very large data sets in one of the most complex data warehouse environments. You will have the opportunity to help business and engineering stakeholders determine what performance metrics they should be tracking and establish new and expand existing automated data collection to feed into the data pipeline. You will regularly apply your analytical and problem solving skills and perform analysis with tools like Jupyter, SageMaker, and Pandas so we better understand customer's file system performance.
Day-to-day you will:
Work closely with product management, sales, and business stakeholders to analyze data from a multitude of sources.
Design, implement, and maintain a data pipeline and analytical environment using third-party and in-house reporting tools, modeling metadata, and building reports and dashboards.
Use creative problem-solving to automate the collection and analysis from available data sources in order to deliver actionable output.
Iteratively improve analysis and identify new metrics to improve analytics.
1+ years of experience as a Data Engineer or in a similar role
Experience with data modeling, data warehousing, and building ETL pipelines
Experience in SQL
1+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets
Demonstrated strength in data modeling, ETL development, and data warehousing
Experience using big data technologies (Hadoop, Hive, Hbase, Spark etc.)
Knowledge of data management fundamentals and data storage principles
B.S. degree in mathematics, statistics, computer science or a similar quantitative field.
Experience in writing complex, optimized SQL queries across large datasets.
Experience with data analysis tools like Jupyter and Pandas.
Experience working with a diverse set of business and engineering stakeholders at all levels
Experience with AWS technologies including Redshift, SageMaker, EMR, RDS, S3, and Kinesis
Demonstrated ability to coordinate projects across functional teams, including engineering, sales, product management, finance, and operations
Proven track record of successful communication of analytical outcomes through written communication, including an ability to effectively communicate with both business and technical teams
Amazon is committed to a diverse and inclusive workforce. Amazon is an equal opportunity employer and does not discriminate on the basis of race, ethnicity, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.