Scentsy is looking for an Applied Data Engineer to provide innovative modernization and ensure the availability, reliability, and performance of Scentsy’s Data Analytics eco-system.
Note: Our company is unable to provide visa work sponsorship for this position.
This job is worked on-site at our headquarters in Meridian, ID.
What You Will Do:
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Assembling large, complex sets of data that meet non-functional and functional business requirements
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Identifying, designing, and implementing internal process improvements, including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
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Building required infrastructure for optimal extraction, transformation, and loading of data from various data sources using AWS and SQL technologies
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Building analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics, including operational efficiency and customer acquisition
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Working with stakeholders including the Executive, Product, Data, and Design teams to support their data infrastructure needs while assisting with data-related technical issues
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Working with the architects, including enterprise, application, and data architects, to design, implement, and support Scentsy’s data lake
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Translating analytical program models, including but not limited to scripting, error handling, and documentation
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Evaluating new technologies for data and technology modernization within Scentsy’s data ecosystem
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Acting as technical lead for data lead projects and initiatives
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Coaching and mentoring to less experienced engineers
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Performing all other assigned tasks and requirements as needed
We're Looking For:
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Graduate and/or bachelor’s degree in Information Systems, Informatics, Statistics, Computer Science or another quantitative field
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5 years of data engineering experience
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Ability to build and optimize data sets, data pipelines and architectures
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Ability to perform root cause analysis on external and internal processes and data to identify opportunities for improvement and answer questions
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Excellent analytical skills associated with working on unstructured datasets
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Ability to build processes that support data transformation, workload management, data structures, dependency and metadata
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Recommends data strategies, ETL processes, and procedures for getting data in and out of the data lake
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Experience in data access and delivery technologies, including familiarity with data quality assessment, data organization, metadata, and data profiling
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Ability to take complex, ambiguous problems, break them down into smaller parts, and problem solve to come up with a whole, integrated, and strategic solution
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Demonstrated understanding and experience using software and tools including relational NoSQL and SQL databases including SQL Server and Postgres
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Hands on experience with AWS Services like Cloud Formation, S3, Glue, Lambda, Event Bridge, SNS/SQS, and others
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Demonstrated ability to self-learn and lead teams into new technologies and engineering methods
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Demonstrated ability to solve technical problems relevant to data engineering using programming languages (SQL and Python)
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Demonstrated ability to solution with AWS services and provision infrastructure from code/template
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Excellent data manipulation and analysis skills
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Excellent skills in SQL, data modeling, data warehousing, data lake, and OLAP
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Excellent written and oral communication skills
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Experience with Bitbucket and TeamCity, a plus
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Experience with Redgate Flyway, a plus
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Familiarity with Agile Framework