• 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.

• Lead, mentor, and develop a high-performing data engineering team, fostering a culture of technical excellence, accountability, and continuous improvement in data pipeline development and delivery. • Design, implement, and maintain scalable, efficient ETL/ELT pipelines across cloud and legacy systems. • Develop robust data models leveraging best practices such as the medallion architecture (Bronze, Silver, Gold layers) to organize raw, refined, and curated data for trusted analytics. • Ensure data workflows and structures are optimized to support analytical, operational, and self-service use cases with high performance, reliability, and maintainability. • Deliver and support seamless data integration in and out of enterprise data platforms ensuring timely, accurate, and secure data availability for reporting, analytics and other data needs. • Drive adoption of best practices in data engineering design and coding standards to ensure scalable, maintainable, and reusable solutions aligned with architectural principles.