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Ethos is a life insurance technology company transforming how life insurance is bought, sold, and underwritten through a three-sided platform serving consumers, agents, and carriers. The company offers instant, accessible products with no medical exams required.
You will join the Data Platform team to build internal infrastructure, services, and tooling that powers the company's data stack. This is a software engineering role with deep data expertise. You will design and operate distributed workflow systems using Temporal and Airflow, build internal platform applications with config-driven UIs that enable teams to author and run their own ingestion and transformation jobs, and write Python libraries consumed by engineers across the company. You will also administer core data infrastructure including Snowflake, dbt Cloud, Airflow, and Kafka, with deep ownership of governance, reliability, and cost optimization.
Your customers are other engineers, analytics engineers, data scientists, and operations teams. The platform you build enables their work. You will own production code end-to-end: design, review, deploy, operate, and respond to incidents. Responsibilities include building and operating Temporal-based workflow systems for long-running, reliable distributed data workflows; writing and maintaining internal Python libraries including shared Airflow operators and frameworks; building internal data platform applications with config-driven UIs; owning cost governance across Snowflake, dbt Cloud, Airflow, and Kafka with monitoring and alerting; administering Snowflake at depth including role hierarchy design, RBAC, data masking, row access policies, network policies, and resource monitors; developing end-to-end automation for service and ML model deployment; implementing tools and processes to monitor production systems and ML model performance; supporting the Analytics Engineering function by building platform capabilities for orchestration, CI/CD, testing frameworks, and metadata; and working closely with software engineering, analytics, operations, business, and product teams to understand and meet platform and data requirements. You will ensure all platform and data warehouse activities adhere to regulatory standards, data privacy rules, and company policies.
Qualifications: 7+ years of software engineering experience; strong proficiency in Go or Python; experience building and maintaining backend systems, internal tools, or platform applications in production; strong understanding of software engineering fundamentals including data structures, algorithms, concurrency, and system design; strong understanding of software development best practices including CI/CD, automated testing, code review, and observability; proficiency in SQL and data modeling; strong understanding of ETL processes, data warehousing, and data governance principles; experience building and managing data-intensive or distributed applications; experience designing and operating APIs, services, and workflow-based systems.
Good to have: experience with Go and Python in production; experience with workflow orchestration platforms such as Temporal; experience building internal developer platforms or business-critical internal applications; experience with MLOps including ML model deployment, monitoring, and lifecycle management; familiarity with MLflow, Kubeflow, or SageMaker; experience with Snowflake, Airflow, and dbt; experience with stream processing frameworks such as Flink; experience with cloud-native infrastructure and containerized deployments; experience with monitoring and observability tooling for production systems.
