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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, democratizing access to life insurance.
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, requiring you to 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.
Key responsibilities include:
- Building and operating internal data platforms, services, and distributed workflow systems (Temporal, Airflow) that support the company's data infrastructure
- Writing and maintaining internal Python libraries, including shared Airflow operators and frameworks
- Building Temporal-based workflow systems for long-running, reliable distributed data workflows
- Creating internal data platform applications with config-driven UIs for self-service job authoring and execution
- Owning cost governance across Snowflake, dbt Cloud, Airflow, Kafka, and other data platform services through monitoring, alerting, and optimization
- Administering Snowflake deeply: role hierarchy design, RBAC, data masking, row access policies, network policies, resource monitors, and cost governance
- Owning production code end-to-end: design, review, deploy, operate, and respond to incidents
- Developing end-to-end automation for service and ML model deployment
- Implementing tools and processes to monitor performance, reliability, and health of production systems and ML models
- Supporting the Analytics Engineering function by building platform capabilities (orchestration, CI/CD, testing frameworks, metadata)
- Working closely with software engineering, analytics, operations, business, and product teams to understand and meet their platform and data requirements
- Ensuring all platform and data warehouse activities adhere to regulatory standards, data privacy rules, and company policies
Your customers are other engineers, analytics engineers, data scientists, and operations teams. The platform you build enables their work. This role is ideal if you enjoy building developer-facing platforms, operating distributed systems in production, and working deeply with the modern data stack.
Qualifications:
- 4+ years of software engineering experience
- Strong proficiency in at least one programming language (Go or Python)
- Experience building and maintaining backend systems, internal tools, or platform applications in production environments
- Strong understanding of software engineering fundamentals: data structures, algorithms, concurrency, and system design
- Strong understanding of software development best practices: 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
Desirable experience:
- Go and Python in production environments
- Workflow orchestration platforms such as Temporal
- Building internal developer platforms or business-critical internal applications
- MLOps, including ML model deployment, monitoring, and lifecycle management (MLflow, Kubeflow, SageMaker)
- Snowflake, Airflow, and dbt
- Stream processing frameworks such as Flink
- Cloud-native infrastructure and containerized deployments
- Monitoring and observability tooling for production systems
