What are the responsibilities and job description for the Platform Engineer position at Teal Omics?
At Teal Omics, we are reshaping the way new precision therapies are discovered for aging-related diseases using advanced multi-omic (genomics, proteomics, metabolomics) technology. Built upon our founder’s scientific work at Stanford University, where they discovered that blood-based molecular signatures can be used to measure the age of individual organs, our focus is to accelerate the development of novel precision medicines by building a first-of-its-kind analytics platform that digitizes our knowledge of the molecular changes that occur throughout aging. The 21st century will be defined by the convergence of science and technology to increase both human lifespan and health span, and we believe our approach can unlock scaled discovery of novel precision therapies across all aging-related diseases.
We are seeking a software engineer with experience building and maintaining GCP environments. The role will be a remote, contract role, and we are looking for individuals who can provide dedicated support to our R&D team.
This role requires experience in both planning and implementing scaled software solutions.
Role Requirements:
- Familiarity and some experience with the core tools (or similar) in our (growing) technology stack: Cloud Storage (GCS or S3), Compute (Google CloudRun, Batch, Kubernetes), database (BigQuer), Postgresql, Python, PySpark, BigQuery, Docker, API backends (Django, Flask or FastAPI). Our current cloud provider is Google Cloud Platform.
- Foundational knowledge of and experience developing and deploying scalable, cloud-based data science and engineering pipelines. If computational DAGs are your friends, you’ll do great. Past experience with workflow orchestration tools like Domino, Kubeflow, Spark (ML environments), Snakemake/Nextflow (bioinformatics), or Dagster/Prefect (general data engineering) will be a plus.
- Experience developing end-to-end technology systems, including data and feature engineering pipelines, model life-cycle support, and data governance.
- Foster processes and technology to boost operational scalability, enabling our teams to rapidly expand capacity.
- Simplify how data scientists access, transform, and use their data by promoting consistent data usage patterns, including version management, shared ontologies & data dictionaries.
- Simplify how data engineers build, maintain, and extend their data pipelines, advising colleagues on data transformations and database design and providing guidance on best practices.
- Hands-on experience implementing technical solutions yourself
- Experience planning and delivering projects that begin with some ambiguity, ideally spanning multiple production systems and involving diverse technology.
- Ability to establish and maintain robust CI/CD pipelines and cloud infrastructure. Infrastructure-as-Code experience is a plus.
- Domain experience in the life sciences is a plus.