• Design, build, and maintain end-to-end data pipelines using Databricks (SparkSQL, PySpark) for data ingestion, transformation, and processing. • Integrate data from various structured and unstructured sources, including medical imaging systems, EMRs, Change-Data-Capture from SQL Databases, and external APIs. • Collaborate with the analytics team to create, optimize, and maintain dashboards in Looker. • Deploy and manage cloud-based solutions on AWS (e.g., S3, EMR, Lambda, EC2) to ensure scalability, availability, and cost-efficiency using IaC tooling (Terraform and Databricks Asset Bundles). • Oversee MongoDB and PostgreSQL databases, including schema design, indexing, and performance tuning. • Adhere to healthcare compliance requirements (e.g., HIPAA) and best practices for data privacy and security. • Work cross-functionally with data scientists, product managers, and other engineering teams to gather requirements and define data workflows.

• Build and maintain robust data pipelines using tools like Airflow, Airbyte, and BigQuery. • Manage and evolve our data warehouse architecture. • Integrate third-party platforms including Salesforce, HubSpot, Iterable, ZocDoc, and Metabase. • Lead the implementation of centralized data governance, defining sources of truth and managing data flow standards. • Partner with cross-functional teams to implement clean, reporting-ready data models in dbt. • Write clean, production-grade code in a general-purpose language (e.g., Python). • Monitor data quality and ensure reliability across systems.

• Lead, mentor, and grow a team of data engineers • Partner with cross-functional teams to deliver trustworthy, actionable data • Drive the long-term vision for data engineering practices • Enable AI-driven approaches to improve development efficiency

• Designing, building, iteratively improving, and fully automating the data pipelines and algorithms we use for processing raw flow cytometry data from our highly multiplexed bead-based assays into quantitative protein measurements. • You will leverage your fundamental knowledge of biosensors, fluorescence data, and bioengineering R&D to act as an expert for the interpretation, and analysis of, nELISA experimental data when challenges arise in R&D and day-to-day Lab Operations. • You will also support R&D and Lab Operations teams through developing additional data support features and algorithms to support the growth of Nomic going forward. • This role will involve substantial communication, teamwork, and attention to detail, especially when identifying and troubleshooting issues related to nELISA data and ensuring we build the right tools, and the right abstractions. • When tooling does not yet exist, you will leveraging your technical and bioscience domain expertise to develop new data analysis pipelines.