The Role Lyrebird is building a trusted, scalable data platform to support how teams across the business make decisions. In this role, you’ll contribute to the development of data pipelines and analytics-ready tables, help maintain core data models, and support self-serve reporting used by product, finance, and go-to-market teams. You’ll work alongside experienced data and engineering partners to turn business questions into clear, reliable data solutions, while learning best practices around data quality, testing, and observability as the platform grows. This is a hands-on role for someone early in their data engineering career who enjoys writing code, learning modern data tools, and making data easier for others to use. About Us Lyrebird Health is transforming the quality and accessibility of healthcare by automating clinicians’ most time-consuming tasks. Thousands of clinicians across multiple disciplines use Lyrebird every day, and that number continues to grow rapidly. Clinicians trust us to deliver a fast, reliable, and secure experience in high-pressure environments where accuracy matters. We take that responsibility seriously. Our focus is on earning and maintaining that trust while continuing to build products that genuinely improve how care is delivered. We’re an ambitious, fast-moving team with a high bar for quality, clarity, and ownership. We value thoughtful decision-making, direct communication, and people who care deeply about the impact of their work. What you'll do Build end-to-end data pipelines from source systems to analytics-ready tables. Design and maintain core data models that power product analytics, finance reporting, and dashboards. Work with cloud data warehouses (BigQuery, Snowflake, Databricks) to deliver scalable solutions. Use code-based transformation tools (e.g. dbt, AWS Glue) to create reliable, tested data models. Collaborate closely with product, engineering, and business stakeholders to understand requirements and define data solutions. Enable self-serve analytics by supporting BI tools like Looker, Sigma, and SQL-based reporting. Contribute to CI/CD pipelines for data using GitHub and GitHub Actions. Improve data quality, reliability, and observability as the platform scales. What you'll bring Strong fundamentals in SQL and Python. Solid understanding of data warehousing and data modeling concepts. Hands-on experience building pipelines on cloud data warehouses (BigQuery, Snowflake, or Databricks). Experience with code-driven data transformation tools (dbt, AWS Glue, or similar). Experience working with modern BI tools (Looker, Sigma, or SQL-based tools). Comfortable using GitHub for version control and GitHub Actions for CI/CD. Strong communication skills — able to clearly explain data concepts and trade-offs to both technical and non-technical audiences. Stakeholder management experience — comfortable gathering requirements, aligning on definitions, and managing expectations in a fast-moving environment. Nice to have Experience with product analytics or event-based data. Exposure to finance or revenue data (subscriptions, billing, metrics). Familiarity with DataOps paractices and multi-environment deployments. Experience with Infrastructure as Code (Terraform or similar). Knowledge of data monitoring and observability tools. At Lyrebird, you don’t just write code — we help shape the future of the human experience. If you want to pioneer, to create, and to see your work touch lives directly, we’d love to hear from you. We’re building a team that reflects the diversity of the people who’ll benefit from our work. We want Lyrebird to be a place where everyone feels safe, supported, and able to thrive. If you’re from an underrepresented background in tech, we especially encourage you to apply — even if you don’t meet every single requirement. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
