Sr. Data Engineer

Lyra Health is a leading provider of evidence-based mental health care, serving millions globally. They are seeking a Sr. Data Engineer to build and scale their data infrastructure, which powers analytics and research platforms that drive product development and clinical outcomes.

Responsibilities:

• Join a team of innovative engineers building and scaling the core data infrastructure, pipelines, and services that power our products
• Design and implement a robust data warehouse to support a wide range of analytics and operational use cases
• Develop and maintain efficient data pipelines and curated data sets by working closely with stakeholders to gather requirements and translate them into technical solutions
• And of course—write code every day!

Requirements:

• 4+ years of experience as a Data Engineer
• Proven track record of writing high-quality, production-ready Python and delivering impactful, scalable data projects
• Expertise in SQL-based data modeling and transformation frameworks (e.g. dbt), with a focus on schema management, performance, and data governance
• Strong experience with modern ingestion tools: configuring, deploying on data integration platforms (e.g. Airbyte), commercial ELT pipelines (e.g. Fivetran), and database replication methods like Change Data Capture (CDC)
• Strong orchestration background: hands-on experience building robust, fault-tolerant pipelines using Apache Airflow (including custom alerting, advanced logging, and automated retry mechanisms)
• Experience with modern data visualization and analytics tools such as Sigma or Tableau
• Strong knowledge of Snowflake Cloud Data Warehouse Architecture, including native ingestion patterns (e.g. Snowpipe for continuous streaming) and open table formats (e.g. Apache Iceberg)
• Strong knowledge of Snowflake administration including familiarity with maintaining security compliance, role based access control (RBAC), external authentication methods, and managing downstream data consumption in external platforms
• A 'QA-First' engineering mindset: Strong experience in end-to-end data quality assurance, regression testing, and data validation. Capable of troubleshooting data discrepancies, and performance-tuning complex pipelines
• Strong foundational knowledge of relational databases, core data warehousing principles, and dimensional modeling techniques
• Experience working with sensitive data (such as PII/PHI) within healthcare or similarly regulated environments, implementing robust data masking or redaction processes
• Familiarity with DevOps principles and infrastructure automation using Terraform
• A thoughtful approach to balancing high-quality engineering standards with tight deadlines
• Excellent communication skills with a talent for building consensus and distilling complex technical problems into clear, actionable business priorities