Staff Data Engineer - ML Infrastructure

Staff Data Engineer - ML Infrastructure

Why We Need You:

We’re looking for a staff-level engineer and a proven technical leader passionate about building infrastructure for data scientists to run machine learning models with big data in production successfully. We expect you to collaborate with data scientists and full-stack engineers to drive alignment on design patterns and best practices to support the business. If you are technical, creative, future-focused and excited about fostering an environment amongst our engineers that helps create avenues for success and learning, then this is an excellent opportunity for you.

How you contribute to our vision: Key Responsibilities

  • Build and improve the capabilities of the data platform that enable and accelerate the production of machine learning (ML) based solutions
  • Drive and define standards for data engineering across the organization. 
  • Provide guidance, technical leadership, and mentoring to other members of the team
  • Proactively recommend improvements and new approaches addressing potential systemic pain points and technical debt
  • Anticipate technical demands on the data platform based on the organization’s roadmap and systematically drive the evolution of the architecture towards those ends
  • Mentor junior members and participate in scaling up the existing team 

Minimum Requirements

  • You have 8+ years of experience building, designing, and evolving data architecture for large scale systems 
  • Experience working with Product teams ensuring and driving a timely delivery
  • Have a deep understanding of the trade-offs to be considered when designing and delivering machine learning solutions to production
  • Demonstrated experience with data modeling, database design, extract transform load (ETL) processes, working with unstructured data, and cloud-based data infrastructure
  • Awareness of and experience with ML processes (exploration, training, deployment), technologies (services, packages), and infrastructure (AWS)
  • Passionate about data/ML engineering and interested in driving discussions with stakeholders and executives
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