We are looking for passionate Business Intelligence Engineer (BIE) to scale our spoken language understanding (SLU) analytics product suite. The product itself is internally built and own analytics software, specifically for analyzing automatic speech recognition (ASR) and natural language understanding (NLU) component and system performance. We are developing software to make analyzing baseline performance and identifying improvement areas easier for our Alexa teams.
The BIE will own optimizing the underlying SQL queries and ETLs so our software services can consume disparate and large data sources into our analytics suite. As a BIE leader within Alexa, we look to you for design, implementation, and successful delivery of large-scale, critical, and/or difficult data solutions involving a significant amount of work. You will be a part of a team of top technical professionals developing complex systems at scale and with a focus on sustained operational excellence. Where needed, you integrate your team's data solutions with those owned by other teams. You influence your team's technical and business strategy by making insightful contributions to team priorities and overall data approach. You take the lead in identifying and solving ambiguous problems, architecture deficiencies, or areas where your team's current queries, data models/structure, etc. slow the team down. You make data solutions simpler. We are looking for people who are motivated by thinking big, moving fast, and changing the way our internal users analyze information to drive Alexa customer engagement. If you love to implement solutions to hard problems while working hard, having fun, and making history, this may be the opportunity for you.
The Business Intelligence Engineer:
• Will build and optimize ETLs and their underlying SQL queries to efficiently, accurately, and with low-latency load data to our product suite for use by customers.
• Will influence big data solutions/access to data set(s) in team architecture and will be solely responsible for the efficient, secure, and performant queries underlying our analytics suite. As such, attention to detail, data integrity, and strong analytical skills (i.e., understanding business implications of what the data says) are required.
• Has knowledge of engineering and operational excellence best practices. Can make enhancements that improve data processes (e.g., data auditing solutions, management of manually maintained tables, automating, ad-hoc or manual operation steps).
• Understands how to make appropriate data trade-offs. Can balance customer requirements with technology requirements. Knows when to re-use code. Is judicious about introducing dependencies.
• Delivers pragmatic solutions. You do things with the proper level of complexity the first time (or at least minimize incidental complexity).
• Understands how to be efficient with resource usage (e.g., system hardware, data storage, query optimization, AWS infrastructure etc.)
• Will write code that a Data Engineer or Software Development Engineer unfamiliar with the system can understand.
• Communicates proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions
• Will thrive in an agile, iterative environment where collaboration with Product Managers, Data Engineers, and Software Development Engineers is crucial for delivering the right information to our customers.
• Should have demonstrated success in an environment which offers ambiguously defined problems, big challenges, and quick changes.
• Bachelor's degree or higher in a quantitative/technical field (e.g. Computer Science, Statistics, Engineering)
• 3+ years of relevant experience in one of the following areas: Business intelligence, data engineering, or business analytics
• 3+ years of hands-on experience in writing complex, highly-optimized SQL queries across large data sets
• Experience working with enterprise reporting systems, data analytics.
• Experience with Tableau, Matillion, and AWS services (Redshift, S3, AWS Glue, EMR, DynamoDB)
• Knowledge of distributed systems as it pertains to data storage and computing
• Master's degree in a quantitative/technical field (e.g. Computer Science, Statistics, Engineering)
• Has knowledge of recent advances in distributed systems (e.g. MapReduce, MPP architectures, and NoSQL databases). You are proficient in a broad range of data design approaches and know when it is appropriate to use them (and when it is not).
• Demonstrable experience in scripting languages (Python, Perl, Ruby) and Excel
• Demonstrated strength in data modeling, ETL development, and Data warehousing
• Experience working as an Analytics Engineer or Data Scientist working with cross functional teams.
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