Data Platform Engineering for Scalable Discovery & Insight
We design and build data platforms that handle complexity—at scale. Whether your workflows involve biosignals and omics, behavioral analytics, business telemetry, or instrument-integrated pipelines, we engineer modular systems that balance performance, flexibility, and usability.
What We Build
- Resilient, real-time and batch ETL pipelines
- Flexible data storage across structured, unstructured, and streaming sources
- Data curation tools with human-in-the-loop review and versioning
- Dashboards and ad-hoc interfaces for insight and decision support
- Integrated analysis pipelines and model-ready datasets
Ideal For
- Scientific and operational data teams across biotech, pharma, materials, and energy
- AI/ML teams preparing and scaling high-quality training data
- Digital health, fintech, and logistics startups needing robust analytics platforms
- Product and research teams building multimodal, ML-ready pipelines
Recent Engagements
- Built a cross-modality data suite integrating physiology, behavior, imaging, and omics
- Scaled ETL pipelines across cloud/HPC for high-throughput behavioral and image data
- Developed visualization tools and harmonized databases for translational discovery
- Engineered synchronized data acquisition systems with real-time experiment control
Core Components of a Modern Data Platform
Data Acquisition
Ingest structured, unstructured, and streaming data from internal and external sources.
Scalable Storage
Organize diverse data types using cloud-native lakes, warehouses, and hybrid models.
Processing Pipelines
Build ETL/ELT workflows for cleaning, transformation, enrichment, and feature extraction.
Curation Interfaces
Enable human-in-the-loop review with visual tools and audit-ready workflows.
Analysis & Visualization
Deliver dashboards and tools for routine monitoring and ad-hoc exploration.
Operationalization
Promote useful insights and tools into production: models, metrics, decision support.