Senior Data Science Platform Engineer

Summary

We are looking for a highly skilled and experienced Senior Data Science Platform Engineer with a strong focus on platform software engineering to join our innovative precision medicine team. In this role, you will lead the design, development, and optimization of computational platforms and data pipelines on Amazon Web Services (AWS). You will be at the forefront of integrating multi-omics data processing with UI and data warehouse implementation, ensuring our platforms and tools are robust, scalable, and efficient for large-scale genomic and clinical data analysis.

Responsibilities

• Data Engineering: Design, develop, validate, and maintain scalable data analytic pipelines for large-scale genomic and patient cohort datasets. Develop data ETL processes to ensure efficient and reproducible data flow and integration across platforms.
• Software Development: Lead the development of data warehouse and data visualization applications, ensuring they are well-integrated with data pipelines and platforms. Collaborate with software engineering team to apply best practices in software development, including version control, code review, testing, and documentation.
• Cloud Infrastructure Management: Architect, build, and manage AWS cloud infrastructure using Infrastructure as Code (IaC) tools like Terraform or CloudFormation. Optimize AWS services (e.g., EC2, S3, Lambda, RDS, ECS/EKS) for data-intensive bioinformatics workflows.
• Platform Performance Optimization: Monitor and enhance platform and pipeline performance, focusing on scalability, efficiency, and cost-effectiveness. Develop strategies for high availability, data redundancy, and disaster recovery.
• Automation and CI/CD Pipelines: Develop and maintain automation scripts and CI/CD pipelines to streamline the deployment of software applications and data pipelines. Ensure updates and new releases are managed efficiently and with minimal disruption to operations.
• Data Governance and Compliance: Implement and maintain data governance and data quality frameworks. Enforce data security best practices and ensure all platforms and pipelines comply with relevant regulations (e.g., HIPAA, GDPR) for handling sensitive genomic and patient data.
• Collaboration and Mentorship: Collaborate with bioinformaticians, data scientists, and other stakeholders to understand computational and data requirements and deliver high-quality data solutions. Mentor junior engineers and foster a culture of continuous learning and improvement within the team.
• Documentation and Training: Maintain comprehensive documentation of platform architecture, data pipelines, and software development processes. Provide training and support to team members and stakeholders on platform and pipeline usage. Stay up-to-date with the latest industry trends and technologies to continuously improve data engineering practices.

Experience and Qualifications

• Bachelor’s or Master’s degree in computer science, data engineering, or a related field.
• Five or more years of experience in data solution development, with a focus on cloud-based platforms and data engineering. Biotech or pharmaceutical industry experience strongly preferred.
• Five or more years of experience in managing and optimizing AWS infrastructure, with a strong track record of using services like EC2, S3, Lambda, RDS, ECS/EKS, and VPC.
• Strong background in data engineering, including designing ETL processes and working with large-scale genomics and biological datasets.
• Proven experience with Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or AWS CDK.
• Extensive experience in software development, including proficiency in Python, Java, JavaScript, or similar programming languages. Extensive experience in web interface application and data warehouse development.
• Certifications: AWS Certified Solutions Architect (Professional) or AWS Certified DevOps Engineer preferred.

Skills and Abilities

• Expertise in data engineering tools and technologies, such as Apache Spark, Hadoop, and data lakes.
• Proficiency in programming languages such as Python and R for data analysis and pipeline development.
• Strong experience in Linux/Unix systems administration and shell scripting.
• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Strong experience with CI/CD tools and practices, such as Jenkins, GitLab CI, or AWS CodePipeline.
• Familiarity with database management systems (SQL and NoSQL) and data warehouse solutions.
• Familiarity with web interface frameworks (Django or Flask).
• Knowledge of data security and compliance standards (HIPAA, GDPR) related to genomic and clinical data.
• Strong analytical and problem-solving skills, with a proactive approach to identifying and solving technical challenges.
• Familiarity with bioinformatics tools, workflows, and data formats (e.g., FASTQ, BAM, VCF, Bioconductor).
• Excellent leadership and communication skills, with the ability to collaborate effectively with both technical and non-technical stakeholders.

The expected base pay range for this position is $125,000 - $195,000 plus bonus, equity, and comprehensive benefits. The base pay range reflects the target range for this position, but individual pay will be determined by additional factors such as job-related skills, experience and relevant education or training. This range may be modified in the future.

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