BioTech

Technology at the intersection of biology and computation

⬢ ティア 3専門分野

+$30k-

給与インパクト

12か月

習得までの期間

難しい

難易度

職種数

概要

Biotech engineers build genomic data pipelines, clinical trial systems, and lab information management software. A domain specialization at the intersection of life sciences and tech, commanding +$30k-$50k salary premiums. Key platforms: Galaxy, Nextflow, Snakemake, BioPython, AWS HealthOmics, Benchling. Path: molecular biology fundamentals → bioinformatics tools → genomics pipelines → FDA regulatory knowledge → AI drug discovery over 12-15 months.

BioTechとは

BioTech domain knowledge for software professionals covers bioinformatics platforms, drug discovery software, genomics data pipelines, lab information management systems (LIMS), and computational biology tools. Tech roles in biotech command premium salaries due to the specialized knowledge required. The convergence of AI and biology (AlphaFold, AI drug discovery) is creating explosive demand for engineers who can bridge software and life sciences.

🔧 ツール＆エコシステム

BioPythonBioconductorGalaxyNextflowSnakemakeCromwellAWS HealthOmicsBenchlingLabKeyVeeva VaultSynthace

📋 始める前に

Python Sql Databases

💰 地域別給与

地域	ジュニア	ミドル	シニア
USA	$105k	$165k	$250k
UK	£65k	£95k	£155k
EU	€70k	€100k	€165k
CANADA	C$110k	C$170k	C$265k

🎓 認定資格

CITI Program (Collaborative Institutional Training Initiative)Coursera Bioinformatics Specialization AWS Healthcare and Medical Devices Veeva Vault Administration Certification

🎯 BioTechを使う職種

Bio Computing Wetware Engineer

Biochemist

Biohacker Quantified Self Expert

Bioinformatics Scientist

Biologist

Biotech Researcher

Chemical Engineer

Clinical Data Manager

Clinical Trial Manager

Data Scientist

Devops Engineer

Hr Generalist

⚖ 比較

Healthcare It Machine Learning Ai Cleantech

❓ よくある質問

Do I need a biology degree to work in biotech?

No. Self-taught engineers with strong bioinformatics skills are in high demand. You need to learn molecular biology fundamentals, bioinformatics tools, and genomics data formats, degrees accelerate this but aren't required. CS backgrounds actually have advantages: you understand scalable data systems better than most biologists.

What's the difference between bioinformatics and lab software?

Bioinformatics = data analysis (genomics pipelines, sequence alignment, machine learning on genetic data). Lab software = operational systems (LIMS, electronic lab notebooks, experiment tracking). Both hire engineers; bioinformatics pays more (+$20k) but requires deeper domain knowledge.

How does FDA regulation affect my work?

If your code touches patient data or drug approval, FDA 21 CFR Part 11 compliance is mandatory. Requires validated software, audit trails, digital signatures, change control. Most biotech jobs require understanding GxP (Good Practice) compliance, not as a lawyer, but as an engineer who knows what's auditable.

What salary jump does biotech experience bring?

Backend dev ($100-130k) → Bioinformatics engineer ($165-220k) is +$40-90k in one year. Biotech supply is thin (few engineers know both CS and biology), and demand from AI drug discovery companies is exploding. This premium holds through 2027-2028.

Which framework should I learn first, Nextflow or Snakemake?

Nextflow is more production-ready and has better cloud support (AWS, GCP, Azure). Snakemake is easier to learn and popular in academia. Start with Snakemake for fundamentals (2-3 weeks), then move to Nextflow for industry roles. Both are essential; knowing both doubles your appeal.

How is AlphaFold changing bioinformatics jobs?

AlphaFold solved protein structure prediction, collapsing 10+ years of research into inference. This destroyed some research jobs but created huge demand for engineers who can integrate AlphaFold into drug discovery pipelines, build datasets for fine-tuning, and handle the computational infrastructure. Job market shifted from pure prediction to applied AI systems.

What's the biggest difference between pharma and biotech startups?

Pharma = regulated, slow, high compliance burden. Startups = fast iteration, lower compliance overhead, but higher technical ambition (AI-first drug discovery, cell+gene therapy automation). Both pay well; startups move faster and have more autonomy.