Senior Data Engineer

About the role

Ethos is seeking a Senior Data Engineer to join our Data Platform team and build the internal infrastructure, services, and tooling that the rest of the company's data stack runs on. This is a software engineering role with a deep data background. You will design and operate distributed workflow systems on Temporal and Airflow, build internal platform applications (including config-driven UIs that let teams author and run their own ingestion and transformation jobs), and write Python libraries that other engineers across the company consume. You will also administer the data infrastructure we own, such as Snowflake, dbt Cloud, Airflow, and Kafka, with deep ownership of governance, reliability, and cost.

Your customers are other engineers, analytics engineers, data scientists, and operations teams. The platform you build is what makes their work possible. If you enjoy building developer-facing platforms, operating distributed systems in production, and going deep on the modern data stack, we'd like to talk to you.

Duties and Responsibilities:

• Build and operate internal data platforms, services, and distributed workflow systems (Temporal, Airflow) that the rest of the company's data stack runs on.
• Write and maintain internal Python libraries (including shared Airflow operators and frameworks) consumed by other engineering teams.
• Build and operate Temporal-based workflow systems for long-running, reliable distributed data workflows.
• Build internal data platform applications, including config-driven UIs that enable users to author and run their own ingestion and transformation jobs.
• Own cost governance across Snowflake, dbt Cloud, Airflow, and Kafka and other data platform services, including monitoring, alerting, and optimization initiatives.
• Administer Snowflake at depth: role hierarchy design, RBAC, data masking and row access policies, network policies, resource monitors, and cost governance.
• Own production code end-to-end: design, review, deploy, operate, and respond to incidents.
• Develop end-to-end automation for service and ML model deployment, ensuring smooth transition from development to production.
• Implement tools and processes to monitor the performance, reliability, and health of production systems and ML models.
• Support the Analytics Engineering function by building the platform capabilities (orchestration, CI/CD, testing frameworks, metadata) that data marts are built on.
• Work closely with software engineering, analytics, operations, business and product teams, to understand and meet their platform and data requirements.
• Ensure that all platform and data warehouse activities adhere to regulatory standards, data privacy rules, and company policies.

Qualifications and Skills:

• 7+ years of experience in software engineering.
• Strong proficiency in at least one programming language such as Go or Python.
• Experience building and maintaining backend systems, internal tools, or platform applications in production environments.
• 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 & Python in production environments.
• 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 tools and platforms like 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.

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