Senior Data Engineer

Industry: Artificial Intelligence
Company size: 11-50

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Job description

A leading Venture Capital-funded company led by an internationally recognized expert is looking for a Senior Data Engineer. The engineer will join the core team to architect highly scalable and reliable data pipelines to consume, integrate, and analyze large volumes of data from various sources. The company is building mind-blowing real-time psychological personalization capabilities together with a growing clientele of top insurance carriers and Fortune 500s. Candidates who would like to make their mark on the platform and company while rubbing shoulders with established professionals are suitable for this position. It requires at least 6+ hours of overlap with the Pacific Time zone.


Job Responsibilities:

  • Work closely with colleagues in engineering, data science, product management, and advisors to engineer smart solutions
  • Offer adequate guidance on the system architecture, code structure and quality, technical debt, and testing
  • Showcase strong skills and aptitude to translate requirements into a scalable solution 
  • Consistently drive efficacy to meet the needs of the company and clients, while upholding deadlines
  • Create tools and platforms to allow internal and external teams to perform visualizations and extract insights from big-data platforms
  • Perform automation on the platform

Job Requirements:

  • Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
  • 4+ years of experience in software development
  • At least 2+ years experience in a data-engineering role for commercial products
  • Solid expertise with big-data or machine learning technologies, preferably Spark
  • Exceptional Python (Pandas) skills and some experience with Scala is helpful
  • Strong SQL development skills and a familiarity with NoSQL
  • Comfortable working with development/debugging environment in Linux/Unix
  • Great zeal to work in a fast-paced engineering team and company
  • Prior expertise in deploying services and data pipelines in AWS
  • Track record of working with containers (Docker, Kubernetes)
  • Ability to implementing operational best practices such as monitoring, alerting, and metadata management
  • Good understanding of machine learning and statistics
  • Hands-on expertise in a technical or team leadership role, similarly a desire to grow into one with a pattern of achievement and growth

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