Senior Data Engineer

The role

You will own the design and evolution of the biomedical data platform. You will architect end-to-end data systems and build cloud-native infrastructure for large-scale biomedical datasets.

What you will do

• Architect and lead the design of end-to-end data systems for large-scale biomedical datasets.
• Define and evolve schema-driven data models.
• Establish best practices for data quality, validation, provenance, lineage, and versioning.
• Build and maintain cloud-native data infrastructure.
• Design pipelines that support both batch and streaming access for ML training, evaluation, and inference.
• Partner with ML engineers, scientists, and product leads to translate research needs into data abstractions.
• Make trade-offs around performance, cost, flexibility, and correctness in production systems.
• Provide technical leadership through design reviews, architectural guidance, and mentorship.
• Identify and address systemic risks in data integrity, scalability, and operational complexity.

Required qualifications

• 5+ years of experience in data engineering, platform engineering, or ML infrastructure roles.
• Proven experience designing and operating large-scale, production-grade data pipelines.
• Strong proficiency in Python and data-centric software engineering practices.
• Deep experience with cloud platforms (AWS, GCP, or Azure), including storage, compute, and security primitives.
• Familiarity with distributed data processing and orchestration systems (e.g., Spark, Beam, Ray, Airflow, Dagster).
• Experience supporting ML/AI workloads, including dataset generation, feature pipelines, and reproducible training workflows.
• Strong architectural judgment and the ability to communicate technical decisions clearly across disciplines.

Nice to have

• Prior work with biomedical or life-science data (e.g., omics, assays, molecular representations, clinical or toxicology data).
• Experience with streaming platforms (Kafka, Pub/Sub, Kinesis).
• Exposure to ontology-aware data modeling or schema evolution in scientific domains.
• Infrastructure-as-code and systems experience (Terraform, Docker, Kubernetes).
• Experience in early-stage startups or research-heavy environments.
• Open-source contributions or technical publications.

Benefits

• Competitive compensation, including meaningful equity participation.
• A chance to architect the data backbone of an AI-driven biomedical platform.
• Direct impact on how scientific data is translated into machine intelligence.
• High autonomy, high trust, and ownership over critical systems.
• Flexible remote or hybrid work arrangements.
• A deeply technical, low-ego culture focused on learning and rigor.

About the company

Absentia Labs is building the data and intelligence infrastructure that powers the next generation of biomedical discovery. They work at the intersection of biology, chemistry, machine learning, and large-scale systems.