Senior Data Engineer

About Carrot: Carrot is a global, comprehensive fertility and family care platform, supporting members and their families through many of life's most memorable moments. Trusted by many of the world’s leading multinational employers, health plans, and health systems, Carrot’s proven clinical program delivers exceptional outcomes and experiences for members and industry-leading cost-savings for employers. Its award-winning products serve all populations, from preconception care through pregnancy, IVF, male factor infertility, adoption, gestational carrier care, and menopause. Carrot offers localized support in over 170 countries and 25 languages. With a comprehensive program that prioritizes clinical excellence and human-centered care, Carrot supports members and their families through many of the most meaningful moments of their lives. Learn more at get-carrot.com . The Role: The Senior Data Engineer leads the evolution, reliability, and scalability of the modern data infrastructure across analytics, reporting, and business intelligence. This role is responsible for architecting, building, and maintaining robust, automated data pipelines, orchestrating workflows, integrating diverse data sources, and partnering with stakeholders across the business to deliver secure, compliant, high-quality data solutions. Hands-on responsibilities span the full data lifecycle, from architecting resilient ETL/ELT pipelines and developing real-time and batch data integrations to automating reporting, optimizing technical debt, and enabling advanced analytics. Senior Data Engineers are operational stewards and technical mentors, driving improvements in system maturity, cost efficiency, automation, and collaborative practices. The Team: Our Data Engineering team develops and optimizes scalable data pipelines and cloud infrastructure to enable secure, automated reporting and analytics. We collaborate closely with partner, product, finance, and legal teams to deliver reliable integrations and self-serve solutions that drive business insights and operational efficiency. The team leads technical incident management, data governance, and ongoing improvements while supporting cross-functional mentorship and stakeholder needs. Minimum Qualifications: Expert-level proficiency in Snowflake, dbt, and Python for data modeling, transformation, pipeline development, and advanced analytics. Advanced SQL skills for complex querying, large-scale data model design, and database optimization. Proven experience architecting, building, testing, deploying, and maintaining scalable, automated ETL/ELT pipelines using modern orchestration tools such as Prefect and Airflow. Hands-on administration of cloud-based data warehouse and lake platforms—Snowflake, AWS (S3, Redshift), Google Cloud—including integration of new sources and performance optimization. Strong understanding of secure external data flows through SFTP, Files.com: Next-Generation MFT, SFTP File Sharing , and APIs, with a focus on reliability and compliance. Mastery of version control (Git, GitHub) and structured workflow management (Jira) for code review, audit trails, and operational transparency. Demonstrated ability to automate and simplify complex manual tasks, building robust, reusable pipeline components that reduce operational overhead. Depth in orchestrating workflows with custom scheduling, alerting, dependency management, monitoring, and error resolution. Dependability, adaptability, and a strong collaborative approach to engineering, thriving in dynamic and ambiguous environments. Preferred Qualifications: Extensive hands-on experience in dynamic, fast-paced settings, including startup or rapid-growth environments, delivering technical solutions under shifting priorities. Direct background supporting cross-functional teams—business intelligence, analytics, and data science—by enabling large-scale integrations, multi-tenant reporting, and advanced analytics tooling. Familiarit ... (truncated, view full listing at source)