Azure Data Engineer at GovX, Inc. | Torre

Azure Data Engineer

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Full-time
Compensation
USD95k - 140k/year
location_on
Hybrid (California, Oregon, Washington, Texas, Tennessee, Florida, New York)
Posted about 3 years ago

Responsibilities


GovX was founded in 2011 to offer exclusive benefits to those who serve our country. The GovX membership is comprised of current and former members of the American military, law enforcement, firefighting, medical services, and government personnel. We are dedicated to supporting these communities and to offering unique value to our members, while delivering an authentic platform for brands to reach our growing customer base. As the largest and fastest growing digital platform serving this deserving audience, we are committed to stretching the limits of ecommerce to deliver the best assortment for our members’ on-duty and off-duty needs. The Cloud Data Engineer is part of a Data Management team that is responsible for modernizing and transforming our data and reporting capabilities across our products by implementing a new modernized data architecture. The position will be responsible for day-to-day data collection, transportation, maintenance/curation, and access to the GovX corporate data asset. The Cloud Data Engineer will work cross-functionally across the enterprise to centralize data and standardize it for use by business reporting, machine learning, data science or other stakeholders. Responsibilities: - Crafting and maintaining efficient data pipeline architecture. - Assembling large, complex data sets that meet functional / non-functional business requirements. - Design and maintain schemas. - Create and maintain optimal data pipeline/flow architecture. - Identifying, crafting, and implementing internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. - Working with technical and non-technical stakeholders to assist with data-related technical issues and support their data infrastructure needs. - Working with the team to strive for clean and meaningful data, and greater functionality and flexibility within the team’s data systems. - Design processes supporting data transformation, data structures, metadata, dependency, and workload management.