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