The Amazon Devices Reverse Logistics (RL) team is seeking a highly skilled and motivated Business Intelligence Engineer to design and run a world class Global Supply Chain business intelligence platform. We are responsible for measuring the health and integration of the entire Devices Reverse Logistics Supply Chain.
The team is responsible for providing actionable performance metrics in a format that is easy to digest at the highest levels in the organization. These business metrics highlight areas of risk and opportunity, performance outliers, and assist in making well-informed, data-based business decisions. Using this information, we will help business leaders develop a strategy on what company-wide investments to make and the level of their importance.
As an Business Intelligence Engineer, you will be working in one of the world's largest data warehouse environments. Working in RL, you will help build one of the largest cross-functional databases in Amazon as well as work on Business Intelligence reporting and dashboarding solutions that are used by thousands of users worldwide.
You should have deep expertise in the design, creation, management, and business use of large datasets. An ideal candidate will have excellent communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions.
You should have expertise in designing, implementing, and operating stable, scalable, low cost solutions to flow data from production systems into the data warehouse and into end-user facing applications. You should be able to work with business customers in a fast paced environment understanding the business requirements and implementing reporting solutions.
Above all, you should bring your passion for working with huge data sets and bringing datasets together to answer business questions and drive change.
Key Responsibilities · Interfacing with business customers, gathering requirements and developing new datasets in our RedShift environment · Optimizing the performance of business-critical queries and dealing with ETL job related issues · Tuning application and query performance using Unix profiling tools · Identifying the data quality issues across the various platforms at Amazon · Extracting and combining data from various heterogeneous data sources · Designing, implementing and supporting a platform that can provide ad-hoc access to large datasets · Modelling data and metadata to support ad-hoc and pre-built reporting · Manage and design a business intelligence reporting platform integrated to internal and external Amazon systems
Bachelor's degree or higher in a quantitative/technical field or equivalent experience (e.g. Computer Science, Statistics, Engineering)
5+ years of relevant experience in one of the following areas: Data engineering, database engineering, business intelligence or business analytics
5+ years of hands-on experience in writing complex, highly-optimized queries across large data sets
2+ years of experience in scripting languages like Python etc.
Demonstrated strength in data modeling, ETL development, and Data warehousing. Data Warehousing
Experience with Redshift, Oracle, etc.
Experience with AWS services including S3, Redshift, EMR and RDS
Experience with Big Data Technologies (Hadoop, Hive, Hbase, Pig, Spark, etc.)
Experience in working and delivering end-to-end projects independently
Knowledge of distributed systems as it pertains to data storage and computing
Proven success in communicating with users, other technical teams, and senior management to collect requirements, describe data modeling decisions and data engineering strategy
Experience providing technical leadership and mentoring other engineers for best practices on data engineering
Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
Masters in computer science, mathematics, statistics, economics, or other quantitative fields.