Senior Data Engineer

Absentia Labs; New York (NY); Hybrid; USD 115,000 - 200,000; Full time

Benefits offered by this job

Flexible remote or hybrid work

Equity participation

Competitive compensation

Job summary

Absentia Labs is building an AI-driven biomedical data platform in New York with a Senior Data Engineer who will own the data backbone. You’ll partner with ML engineers, scientists, and product leads in a deeply technical, learning-focused environment.

The role supports flexible remote or hybrid work and is based in New York, NY (hybrid). You’ll shape data models, pipelines, and governance for scientific workloads and enable scalable ML training and inference.

Qualifications

• 5+ years in data/ML infra with ownership of production systems.
• Experience designing and operating production-grade data pipelines.
• Strong Python and data-centric software engineering practices.
• Deep experience with AWS, GCP, or Azure cloud platforms.
• Familiarity with distributed data processing and orchestration tools.
• Experience supporting ML/AI workloads.

Responsibilities

• Architect and lead end-to-end data systems for large-scale biomedical datasets.
• Define and evolve schema-driven data models.
• Set best practices for data quality, provenance, lineage, and versioning.
• Build and operate cloud-native infrastructure (data lakes, warehouses, storage, streaming).
• Design pipelines for batch and streaming access for ML training, evaluation, and inference.
• Translate research needs into durable data abstractions with ML engineers and scientists.
• Make principled trade-offs across performance, cost, flexibility, and correctness.
• Provide technical leadership through design reviews and mentorship.
• Proactively address systemic risks across data integrity and scalability.

Skills

5+ years experience

Production data pipelines

Python

Cloud platforms (AWS/GCP/Azure)

Data modeling

Tools

Spark

Beam

Ray

Airflow

Dagster

Job description

Absentia Labs builds an AI-driven biomedical data platform, and this Senior Data Engineer role is a key part of designing and evolving the data backbone. If you value a deeply technical, low-ego culture focused on learning and rigor, this is an opportunity to exercise strong ownership and architectural judgment while partnering closely with ML engineers, scientists, and product leads. The role supports flexible remote or hybrid work and is based in New York, NY (hybrid).

What you’ll do

• Architect and lead end-to-end data systems for large-scale biomedical datasets, including 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.
• Set best practices for data quality, validation, provenance, lineage, and versioning tailored to scientific and ML workflows.
• Build and operate cloud-native infrastructure including data lakes, warehouses, object storage, and streaming systems with an emphasis on scalability and reliability.
• Design pipelines enabling batch and streaming access for ML training, evaluation, and inference.
• Translate research needs into durable data abstractions through close collaboration with ML engineers, scientists, and product leads.
• Make principled trade-offs across performance, cost, flexibility, and correctness for production systems.
• Provide technical leadership through design reviews, architectural guidance, and mentorship.
• Proactively identify and address systemic risks across data integrity, scalability, and operational complexity.

What you bring

• 5+ years of experience in data engineering, platform engineering, or ML infrastructure roles, with clear ownership of production systems.
• Proven experience designing and operating production-grade data pipelines.
• Strong proficiency in Python and data-centric software engineering practices.
• Deep experience with cloud platforms such as AWS, GCP, or Azure, including storage, compute, and security primitives.
• Familiarity with distributed data processing and orchestration tools, including Spark, Beam, Ray, Airflow, and 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.

Technologies you may use

Python, AWS, GCP, Azure, Spark, Beam, Ray, Airflow, Dagster

Benefits and compensation

• Flexible remote or hybrid work arrangements.
• Deeply technical, low-ego culture centered on learning and rigor.
• Compensation range: USD 115,000–200,000 per year.
• Meaningful equity participation (competitive compensation with equity that enables participation in long-term company success and growth).

Bonus if you have

• Experience working with biomedical or life-science data (for example, omics, assays, molecular representations, clinical or toxicology data).
• Streaming platforms such as Kafka, Pub/Sub, or Kinesis.
• Ontology-aware data modeling or schema evolution in scientific domains.
• Infrastructure-as-code and systems experience with Terraform, Docker, and Kubernetes.
• Experience in early-stage startups or research-heavy environments.
• Open-source contributions or technical publications.