Sr. Bioinformatics Engineer

Sequencing•United States ,

About The Position

We are seeking two Senior Bioinformatics Engineers to develop cutting edge genomic analysis tools and infrastructure for the Bioinformatics team. You build the data analysis and interpretation that helps users uncover meaningful insights about their health, wellness, and ancestry from their DNA. This role requires expertise in cloud technologies, large scale genomic data processing, and software development. Your work helps power the future of personal genomics.

Requirements

5+ years of experience in bioinformatics, computational biology, computer science, or a related field.
Strong hands on experience working with real world genomic datasets and file formats such as VCF, BAM, and FASTQ.
Solid understanding of genomic databases, standards, and annotation resources.
Proficient in Python, SQL, and C#, with a track record of writing clean, maintainable code.
Designed and implemented bioinformatics pipelines and data processing systems in production.
Built scalable, reliable systems on cloud platforms such as AWS, including the use of managed services.
Familiar with containerization and orchestration technologies such as Docker and Kubernetes.
Excellent problem solving skills and exceptional attention to detail.
Fluent in English, both written and verbal, and communicate clearly with technical and non technical stakeholders.

Responsibilities

Design, build, and maintain scalable pipelines for processing and analyzing clinical grade 30x whole genome data.
Support a wide range of genomic data types at consumer scale.
Optimize performance, reliability, and cost of data processing pipelines across AWS and hybrid environments.
Keep throughput high and latency predictable for end users.
Develop production quality services and tools using Python, C#, SQL, and AWS, including Lambda, Glue, Batch, S3, and RDS.
Power Sequencing's DNA apps and reports with the services and tools you build.
Collaborate across product, science, engineering, and UX teams to translate scientific findings into clear, consumer friendly genetic insights that are easy to navigate.
Establish and adhere to best practices for software engineering, data versioning, testing, observability, and documentation across bioinformatics workflows.
Debug complex data and infrastructure issues with a systematic approach.
Operate and improve production systems where correctness and reliability are critical.
Leverage AI tools to increase productivity, code quality, and testing depth.
Deliver high quality, validated output.