doxy.me - telemedicine for all

QA Automation Engineer

• Develop and maintain tools that automate repetitive tasks in engineering workflows, such as bug triage, code quality checks, and automated testing pipelines using tools like Selenium.
• Implement intelligent, agentic workflows using LLMs and other AI frameworks to make decisions that traditionally required human intervention.
• Build and maintain integrations between Continuous Integration/Continuous Deployment (CI/CD) pipelines and other internal systems.
• Work with QA, engineering, and operations to ensure automation tools are secure, maintainable, and scalable.
• Act as a partner to teams to help them adopt automation-first approaches, enabling non-technical employees to automate processes themselves.
• Own core metrics infrastructure and automate the delivery of insights on engineering performance and bottlenecks.

AI Automation Engineer

Data Engineer

• Pipeline & Transformation: Design, build, and maintain scalable, reliable data pipelines and transformation processes from product through to Snowflake.
• Product Analytics Enablement: Collaborate with the Director of Strategy to improve product analytics, enabling deeper insights into product performance and user behavior.
• Data Capture Discipline: Partner with Product Engineering teams to educate, define, catalogue, and implement robust event-tracking standards and best practices.
• Data Quality & Governance: Ensure data integrity and accuracy throughout its lifecycle, minimizing tech sprawl and reducing complexity to drive the smallest needed footprint to do what we need, without over-engineering things.
• Tooling & Observability: Manage and optimize data flow using Segment, Hevo, and AWS-based systems; monitor and troubleshoot using observability tools like Datadog.
• Empower BI & Insights: Provide clean, dependable data feeds to the BI team, enabling rich dashboards and analytics in tools like Sigma.
• Educate & Advocate: Act as an internal champion for data principles, helping engineers understand good data modeling and event design.
• Hands-On Implementation: Be comfortable working anywhere in the stack from directly in product code to add or modify event triggers, through to configuring transformations and rules in Segment, Hevo or equivalent platforms and Snowflake.

AI Automation Engineer

Senior Data Engineer