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Lead Data Engineer - Remote Opportunity (TMG)
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Lead Data Engineer

Remote-Salaried


Must be a current US Citizen or Green Card Holder


Please read the qualifications and required experience below to find out if you have them in order to be considered. (Bolded items are very important)


Day to Day


  • Design and lead development of system architectures to solve immediate needs while setting foundations and building reusable components to accelerate future builds
  • Work alongside the data science team to create and iterate concept prototypes with a focus on progress towards value over perfection
  • Own the organization's technology stack
  • Pursue and manage external partners to provide DevOps and Infrastructure support as well as augment engineering capacity

Qualifications


  • Ability to develop and manage cloud-based architectures to support prototypes and early scaling
  • Considers the balance of development velocity, solution flexibility, and solution robustness when architecting solutions
  • Ability to collaborate with a non-technical audience
  • A value-oriented generalist – a passion to solve problems with technology over a passion for specific technologies
  • Working knowledge of data preparation for machine learning and machine learning operations (model deployment and management)


Required Experience


  • Early-stage venture technical leadership (must have current or recent start-up experience)
  • Minimum of B.S. from a 4 year United States based university
  • Must be proficient and have hands-on working experience with Python, SQL, Javascript and various ETL/DW and data engineering tools.
  • 3+ years technical experience working with Structured and Unstructured Databases and data pipelines that you have built from scratch
  • Responsibility for architecting and leading the development of SaaS solutions
  • AWS/Cloud services and examples of building, optimizing, troubleshooting
  • Data pipeline architectures and tools (such as dbt, Prefect)
  • Working knowledge of applied Machine Learning concepts (feature engineering, regression v. classification, training v. inference)




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