We are seeking a professional to design and build back-end services that support our portfolio of data-centric clinical and analytic applications. These applications leverage cloud computing, big data, mobile, data science, data warehousing, machine learning using state-of-the-art software development applications and frameworks.

• Ensure that cloud-based micro-services adhere to uptime and accuracy targets, are resilient, and scale as data volumes and traffic increase.
• Work closely with the data engineering, platform, and solutions teams to develop applications as required to benefit our practice and patients.
• Collaborate with Product Owners, Product Managers, Architects to translate requirements into code.
• Develop services around data warehousing, big data, cloud computing, business intelligence, analytics, and machine learning.
• Participate in DevOps, Agile, continuous development, and integration frameworks.
• Program in high-level languages such as Go, Python, Java, etc.
• Ensure all appropriate documentation of processes and source code is created and maintained.
• Communicate effectively with peers, leaders, and customers throughout the organization.
• Participate in expert-level troubleshooting and resolve problems through root cause analysis, data, and system investigation.
• Contribute to design and architecture discussions with Principals and Architects.
• Lead targeted cross-functional improvement efforts and mentor more junior software engineers.
• Solve complex problems; take a new perspective on existing solutions.
• Work independently with minimal guidance. You may lead projects or project steps within a broader project or have accountability for ongoing activities or objectives.
• Act as a resource for colleagues with less experience.
  
  

Data Engineering Skills & Experience

• Create, verify, and maintain data replication scripts.
• Create, verify, and maintain data validation, processing, and ingestion pipelines.
• Deploy and automate the execution of data replication scripts and data pipelines in cloud infrastructure.
• Create and maintain data catalogs that describe datasets and their contents (i.e., files, file types, tables/views, columns, fields, etc.).
• Create, verify, and maintain dashboards and reports that characterize ingested datasets.
• Create, verify, and maintain data validation scripts/APIs that verify the production dataset contains the correct number of samples/records, expects values/fields/columns are populated, and values are of the correct data type, format, and range.
• Deploy and automate the execution of data validation scripts/APIs.
• Create and maintain user documentation (dataset descriptions, tutorials, code examples, etc.).
• Define entitlements, user groups, roles, and permissions utilized to grant access to datasets.
  
  

Programming Languages

• Primary pipeline development language will be Python.
• Some datatypes and formats may require the use of other languages (i.e., Java, R, etc.) because the libraries/frameworks/sdks available to work with those datatypes and formats are not available in Python.
  
  

Operating Systems

• Primary operating system for data pipeline execution will be Linux, with data pipelines packaged, deployed, and run as containers.
• Data source systems could be Windows or Linux based.
  
  

Infrastructure

• Primary data platform and data pipeline execution infrastructure will be hosted on Google Cloud Platform (GCP) utilizing cloud-native technologies (i.e., Google Cloud Storage, BigQuery, Google Batch, Dataflow, Cloud SQL, etc.).
• Data will be replicated from various on-premises sources that include laboratory instruments, network shared drives, and Windows desktops attached to instruments.
  
  

Development Tools

• Sprints, features, and tasks will be managed in Azure DevOps.
• Code will be managed and versioned in Azure DevOps-based git repositories.
• Code will be compiled, packaged, and deployed utilizing Azure DevOps build pipelines.
• Data pipelines will be packaged, deployed, and run in Docker containers.
• Docker containers will be stored and versioned in Google Cloud Artifact Repositories.
• Veracode will be utilized to scan source code for vulnerabilities and Prisma Cloud will be utilized to scan containers.
• The standard integrated development environment will be JetBrains (PyCharm, IntelliJ, etc.) or VSCode.
  
  

Preferred Candidates

• Experience working on healthcare, life science, or scientific research projects.
• A degree or domain knowledge in a life science-related field (biochemistry, genetics, biology, etc.).
• Experience with Google Cloud Platform-based infrastructure and services.
• 100% remote.