Senior Data Infrastructure Engineer

About the role and why it’s unique:

• As a Senior Data Infrastructure Engineer on the Infrastructure Engineering team, you’ll be building infrastructure solutions to accelerate the development of Underdog’s data systems and its workflows
• Design, build, and maintain key data infrastructure systems including distributed storage, distributed compute, and data streaming infrastructure while ensuring scalability, reliability, and security
• Accelerate developer productivity by building best in class tooling, data systems, and automation workflows
• Collaborate with engineers and data scientists to understand core system requirements and translate them into scalable technical solutions
• Implement and maintain monitoring, alerting, and observability mechanisms to ensure the health and performance of the data platform and its data quality
• Design and develop ML platforming solutions to accelerate the model development lifecycle and the development of ML inference systems
• Research and keep up to date on emerging data technologies and trends and focus on iteratively implementing them into Underdog’s data systems

Who you are:

• At least 7 years of experience building distributed computing and distributed data storage infrastructure on a cloud environment (e.g. AWS, GCP, Azure)
• Well versed with containerization and orchestration technologies such as Kubernetes, ECS, or Docker
• Strong familiarity data streaming frameworks such as Apache Kafka, Apache Flink, or Kinesis
• Experience working with networking in private cloud environments and enabling cross account VPC peering functionality between multiple cloud accounts
• Expert with DevOps practices such as CI/CD pipelines, and infrastructure-as-code tools (e.g. Terraform, CDK, CloudFormation)
• Advanced proficiency with Typescript, Python, or other OOP languages (at least 2)
• Excellent leadership and communication skills with ability to influence and collaborate with engineering and data science stakeholders
• Experience with building and scaling ML platforming solutions (e.g. Databricks, Sagemaker, Vertex AI) into an existing cloud environment

Even better if you have:

• Strong interest in sports

• Prior experience in the sports betting industry

• Prior experience building data or ML platforms with accelerate computing use cases

Our target starting base salary range for this position is between $150,000 - $170,000, plus target equity. The starting base salary will depend on a number of factors including the candidate’s skills and experience, among other things.

What we can offer you:

• Unlimited PTO (we're extremely flexible with the exception of the first few weeks before & into the NFL season)
• 16 weeks of fully paid parental leave
• A $500 home office allowance
• A connected virtual first culture with a highly engaged distributed workforce
• 5% 401k match, FSA, company paid health, dental, vision plan options for employees and dependents