Data Engineer

Budapest, Hungary /
Engineering /
The Bridge Engineering team at Instructure is looking for a data engineer to help us grow our product, scale our systems and empower our feature teams.
Bridge is a tool that helps people find their place at work, form meaningful relationships with peers and managers, and forge a path towards growth. We’re helping our customers create work cultures people love.

Who We’re Looking For

    • A data expert who can help us manage, structure and maintain production databases at scale.
    • A problem solver who asks questions to get at the core issue that the team is grappling with before deciding on a solution.
    • A pragmatist who knows how to make trade offs to solve challenges while building an architecture that scales for the future.
    • A systematic thinker who can understand how the larger system operates and knows when to take a step back and consider alternative approaches.
    • A team player who loves teaching and learning from others.
    • A technology enthusiast who is familiar with and interested in developing a deep set of skills and tools to solve a variety of different problems.

What You’ll Be Doing

    • Managing production databases for multiple services and database clusters
    • Migrating legacy database systems to managed, cloud-based solutions
    • Responding to data-related incidents and contributing to a continuous improvement cultureImplementing automation to reduce toil and enable healthy data systems by default
    • Creating a vision and guiding strategy for modern data management practices for SaaS companies

What You’ll Need to Know

    • How to run production data systems at scale
    • Deep expertise of PostgreSQL
    • Making use of cloud-based solutions (AWS, Azure, Google Cloud).
    • Prior experience with AWS RDS, Aurora and DynamoDB a plus.
    • Familiarity with use-cases that fit NoSQL data stores like graph databases, key/value stores, object stores (i.e. Athena), etc.
    • Strategies for enabling service teams to own their own data systems via documentation, education and automation
    • System observability through monitoring and alerting
    • How to work with a globally distributed team in multiple time zones
    • How to work with Terraform or another means of storing configuration as code