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About 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.
Responsibilities
- 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.
Requirements
- 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.
- 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.
- 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.
Skills
- Python
- AWS
- GCP
- Azure
- Spark
- Beam
- Ray
- Airflow
- Dagster
- Kafka
- Pub/Sub
- Kinesis
- Terraform
- Docker
- Kubernetes
Location
- Remote
- Hybrid
Work Type
- Remote
- Hybrid
Experience Level
- Senior
- 5+ years
Benefits
- Competitive compensation
- Meaningful equity participation
- High autonomy
- High trust
- 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.
- We work at the intersection of biology, chemistry, machine learning, and large-scale systems, transforming fragmented scientific data into reliable, machine-learning-ready knowledge.
- Biomedical data is dispersed, semi-structured, and inherently noisy, yet deeply interconnected across experiments, assays, compounds, and biological systems.
- Extracting value from this complexity requires deliberate schema design, principled abstractions, and rigorous post-processing pipelines that can support both scientific reasoning and large-scale AI.
- We believe breakthroughs start with strong data foundations.