Senior DataOps Engineer- Remote US

Smile Digital Health

2d•Remote

About The Position

The DataOps Engineer owns data analytics infrastructure at Smile Digital Health: from build and deployment pipelines through to large-scale data processing and analytics environments. This role bridges DevOps and data engineering by provisioning and hardening cloud infrastructure, automating CI/CD workflows, and operating the Spark-based and Big Data systems that power both our internal platform and customer deployments.

Requirements

6+ years in a DevOps, DataOps, or data platform engineering role.
Hands-on experience with Apache Spark and at least one managed Spark platform (Databricks, AWS EMR, GCP Dataproc, or equivalent).
Proficiency in Python; solid working knowledge of Java or Scala applications.
Experience with pipeline orchestration tools such as Apache Airflow, Prefect, or similar.
Strong CI/CD experience with GitLab CI, Jenkins, or GitHub Actions.
Infrastructure-as-code proficiency with Terraform, Ansible, or equivalent (AWS CloudFormation and Azure ARM Templates are a plus).
Solid experience operating Linux systems and public cloud environments (AWS, Azure, GCP, or OCI).
Familiarity with Kubernetes or other container orchestration platforms for data workloads.
Ability to manage multiple workstreams in parallel with strong attention to delivery timelines.
Customer-first mindset with strong written and verbal communication skills.

Responsibilities

Build, maintain, and run CI/CD pipelines and infrastructure-as-code for the Smile Digital Health platform and associated services.
Provision, configure, and operate cloud-based Spark clusters and distributed data processing environments, including hands-on work with orchestration tools such as Airflow, Databricks, or EMR.
Write, test, and maintain data pipelines on the same infrastructure you manage, from environment setup through to production monitoring.
Design and maintain scalable, secure infrastructure templates and deployment automation across AWS, Azure, GCP, or OCI environments.
Investigate and resolve data pipeline and integration issues, providing root-cause analysis and durable fixes.
Monitor running systems and pipelines, respond to incidents, tune performance, and manage cloud infrastructure costs.
Foster an Everything-as-Code culture and promote DataOps best practices across the team.
Assist developers and engineers with deployments and builds as needed.