Senior Data Engineer

Absentia Labs is building a biomedical data platform to support scientific discovery and machine learning. The Senior Data Engineer will own the design and evolution of its data platform, including data models, quality and provenance practices, cloud infrastructure, and pipelines for ML workflows.

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:

• 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