Senior Data Engineer

The Role

As a Senior Data Engineer, you will own the design and evolution of Absentia Labs’ biomedical data platform. You will operate with a high degree of autonomy, making long-horizon architectural decisions while remaining hands-on in implementation.

This role is ideal for an engineer who enjoys working in high-ambiguity, research-driven environments, and who understands that data engineering for AI is as much about representation and correctness as it is about scale.

What You’ll Do

• Architect and lead the design of end-to-end data systems for large-scale biomedical datasets (chemical, biological, toxicology, omics, assay, clinical, and experimental data).
• Define and evolve schema-driven data models that reconcile noisy, semi-structured, and heterogeneous sources into coherent, interoperable representations.
• Establish best practices for data quality, validation, provenance, lineage, and versioning suitable for scientific and ML workflows.
• Build and maintain cloud-native data infrastructure (data lakes, warehouses, object storage, streaming systems) with an emphasis on scalability and reliability.
• Design pipelines that support both batch and streaming access for ML training, evaluation, and inference.
• Partner closely with ML engineers, scientists, and product leads to translate research needs into durable data abstractions.
• Make principled trade-offs around performance, cost, flexibility, and correctness in production systems.
• Provide technical leadership through design reviews, architectural guidance, and mentorship of other engineers.
• Identify and proactively address systemic risks in data integrity, scalability, and operational complexity.

Who You Are

• You are a data engineer who thinks in systems and interfaces, not just pipelines. You are comfortable owning poorly defined problems and converging on robust solutions through thoughtful design and iteration.
• You understand that biomedical data is rarely “clean,” and that schema design, normalization, and semantics are first-order engineering problems-especially in AI-driven settings.

You Likely Have

• 5+ years of experience in data engineering, platform engineering, or ML infrastructure roles, with clear ownership of production systems.
• 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.

Bonus If You Have

• Competitive compensation, including meaningful equity participation, allows you to share directly in the long-term success and growth of the company.
• 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.

What We Offer

• 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.

How to Apply

Please submit your resume and a short note on why this role resonates with you. Links to GitHub, technical writing, or relevant projects are encouraged.