Amazon
Business Intelligence Engineer, Alexa, Gale
Amazon, Seattle, Washington, us, 98127
Job ID: 2750891 | Amazon.com Services LLCWe are looking for passionate Business Intelligence Engineer (BIE) to scale our Alexa analytics product suite. We are developing software to make analyzing baseline performance and identifying improvement areas easier for other internal 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.Key job responsibilities
Participate in bi-weekly sprints, executing a mix of tickets focused on long-term product building (e.g., dashboards built in Quicksight, Tableau, etc.) and short-term (ad hoc) analytics requests (e.g., execute SQL to answer an ad hoc business question).Work with product managers to define technical requirements for delivering Alexa analytics products like root cause of failure, attribution of error, time series, etc. analyses.Troubleshoot & monitor big data -- data pipelines, ensuring data bases and customer-facing analytics products are populated with full and reliable data.Implement and maintain big data operational excellence best practices (alarming, scaling, etc.).Execute data backfills (i.e., replacing look back data if new ML model metrics are produced).Support VP-level Weekly Business Reviews (WBRs) in terms of slide creation and supporting deep dives for why KPIs changed week over week, month over month, etc.Sharing your expertise to improve the team's data models, data pipelines, querying, and more!About the team
Our team owns a self-service analytics platform used by Alexa & AGI developers to identify trends in Alexa performance, assess customer impact, and troubleshoot failure patterns. Our customers include: scientists building and debugging ML models, analysts and researchers measuring the customer experience, developers troubleshooting defects and failures, and data teams building business reports and visualizing metrics.BASIC QUALIFICATIONS
3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience.Experience with data visualization using Tableau, Quicksight, or similar tools.Experience with data modeling, warehousing and building ETL pipelines.Experience in Statistical Analysis packages such as R, SAS and Matlab.Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling.Experience writing complex SQL queries.PREFERRED QUALIFICATIONS
Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift.Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets.
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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.Key job responsibilities
Participate in bi-weekly sprints, executing a mix of tickets focused on long-term product building (e.g., dashboards built in Quicksight, Tableau, etc.) and short-term (ad hoc) analytics requests (e.g., execute SQL to answer an ad hoc business question).Work with product managers to define technical requirements for delivering Alexa analytics products like root cause of failure, attribution of error, time series, etc. analyses.Troubleshoot & monitor big data -- data pipelines, ensuring data bases and customer-facing analytics products are populated with full and reliable data.Implement and maintain big data operational excellence best practices (alarming, scaling, etc.).Execute data backfills (i.e., replacing look back data if new ML model metrics are produced).Support VP-level Weekly Business Reviews (WBRs) in terms of slide creation and supporting deep dives for why KPIs changed week over week, month over month, etc.Sharing your expertise to improve the team's data models, data pipelines, querying, and more!About the team
Our team owns a self-service analytics platform used by Alexa & AGI developers to identify trends in Alexa performance, assess customer impact, and troubleshoot failure patterns. Our customers include: scientists building and debugging ML models, analysts and researchers measuring the customer experience, developers troubleshooting defects and failures, and data teams building business reports and visualizing metrics.BASIC QUALIFICATIONS
3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience.Experience with data visualization using Tableau, Quicksight, or similar tools.Experience with data modeling, warehousing and building ETL pipelines.Experience in Statistical Analysis packages such as R, SAS and Matlab.Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling.Experience writing complex SQL queries.PREFERRED QUALIFICATIONS
Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift.Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets.
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