Senior Platform Engineer, Data

Thyme Care•Remote, United States

7d•$175,500 - $195,000

About The Position

As a Senior Platform Engineer on the Data Platform team, you will work on a blend of platform and devex engineering – owning key systems and tooling to ensure both our data pipelines and tooling are reliable and scale as Thyme Care continues to grow. You will join an organization that has prioritized the developer experience from day one and believes that our data teams are most effective when they have the right tools for the job. You will partner with our data, analytics, and actuarial teams to scale the tooling and infrastructure to support faster, more accurate insights that directly impact the members we serve.

Requirements

5+ years of experience building, shipping, and supporting software in production.
Experienced Python engineer who has built and operated systems on AWS, including internal tools and complex job orchestration with Dagster or Airflow.
Taken ownership of features from idea to production, including design, implementation, testing, and ongoing improvements.
Deployed and operated cloud infrastructure with infrastructure-as-code (e.g. Terraform), and have built or contributed to the CI processes your team ships through.
Used and contributed to AI tooling that supports developer productivity, code quality, and engineering best practices.
Worked as a data or platform engineer in a modern data stack environment, ideally in a fast-moving, early-stage or growth-stage company.

Nice To Haves

Hands-on experience with Databricks or Snowflake.
Built, deployed, and maintained dbt at scale.
Prior experience working in healthcare or health tech.

Responsibilities

Lead our team’s orchestration consolidation to Dagster end-to-end, from technical design through implementation and rollout.
Contribute to our custom dbt-core infrastructure that has scaled to over 50 dbt projects and 15 thousand models.
Design and implement the Python abstractions our data tooling is built on, so that a new project or pipeline inherits consistent structure and conventions instead of reinventing them.
Develop proactive monitoring and alerting for our tooling and data pipelines.
Take ownership of both technical solutions and the growth of the engineering team by mentoring junior engineers and fostering a culture of learning, collaboration, and technical excellence.
Make Databricks easier to build on, developing the tooling, defaults, and guardrails that let our teams run their own workloads without needing to become platform experts.