• 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.

• Design, develop, and maintain a scalable data warehouse/lakehouse environment. • Design and implement ELT pipelines to ingest, transform, and deliver high-quality data for analytics and reporting, incorporating current best practices, such as “pipelines as code”. • Ensure data security and compliance, including role-based access controls for security, encryption, masking, and governance best practices to ensure compliant handling of sensitive information. • Optimize performance of data workflows and storage for cost efficiency and speed. • Partner with engineers, analysts, and stakeholders to meet data needs; balance cost, performance, simplicity, and time-to-value while mentoring teams and documenting standards. • Define and implement robust testing frameworks, enforce data contracts, and establish observability practices including lineage tracking, SLAs/SLOs, and incident response runbooks to maintain data integrity and trustworthiness. • Monitor, troubleshoot, and resolve data & automation issues. • Collaborate within an Agile-Scrum framework and develop comprehensive technical design documentation to ensure efficient and successful delivery. • Serve as a trusted expert on organizational data domains, processes, and best practices.

• Design, build, and maintain robust ETL/ELT data pipelines for batch and streaming workloads. • Implement data ingestion and transformation workflows using Apache Airflow, Apache NiFi, Apache Spark, and Kafka. • Integrate data from multiple sources including REST APIs, files, relational databases, message queues, and external SaaS platforms. • Optimize pipelines for performance, scalability, reliability, and cost efficiency. • Develop and operate a centralized data integration hub that supports multiple upstream and downstream systems. • Build reusable, modular integration components and frameworks. • Ensure high availability, fault tolerance, and observability of data workflows. • Design and manage data warehouses, data lakes, and operational data stores using PostgreSQL and related technologies. • Implement appropriate data modeling strategies for analytical and operational use cases. • Manage schema evolution, metadata, and versioning. • Implement data validation, monitoring, and reconciliation mechanisms to ensure data accuracy and completeness. • Enforce data security best practices, access controls, and compliance with internal governance policies. • Establish logging, alerting, and auditability across pipelines. • Automate data workflows, deployments, and operational processes to support scale and reliability. • Monitor pipelines proactively and troubleshoot production issues. • Improve CI/CD practices for data engineering workflows. • Work closely with data scientists, analysts, backend engineers, and business stakeholders to understand data requirements. • Translate business needs into technical data solutions.