Data Engineer

About the role

About us:

Nomic was founded with the purpose of making biology easier to measure. To do this, we have untangled some of the most difficult problems in protein profiling. Our team is combining DNA nanotechnology, high-dimensional flow cytometry, laboratory automation, and machine learning to develop the world’s highest throughput proteomic platform: the nELISA.

Since spinning out of McGill University, we have partnered with and provided platform access to dozens of drug discovery groups including GSK, 4 of the top 10 pharmas, and leading biotechs. We have recently launched a state of the art manufacturing and protein profiling facility that enables multiplexed measurement of >2.5M samples a year, generating an effective 500M protein assays in the process.

We are building a diverse team of engineers, scientists, and world-changers. We like to break down difficult problems using a first principles approach, often leveraging the latest breakthroughs from across the scientific and technological spectrum to drive our mission forward.

Nomic is headquartered in Montreal, Canada with a satellite research lab in Boston, Massachusetts. The majority of our team is based in Montreal and works in a new, shared office/laboratory space with an in-person, but flexible work-from-home, policy.

About the role:

Our Data team’s mission is to build, operate and maintain the data infrastructure and data pipelines needed for analyzing nELISA data at scale. Strong fundamentals in data science and data engineering is key to our vision of the future, and every aspect of our company today is geared towards generating more useful proteomic data. In the lab we are scaling the nELISA to generate higher plex and lower cost proteomic data points, and outside the lab we help our users to best leverage nELISA data for their biological research.

Our data roadmap includes building robust pipelines for decoding nELISA datasets, generating advanced and application-specific bioinformatic pipelines to help customers understand their unique datasets, and developing improved internal-facing tools that will let us execute faster in the lab by extracting insights from our nELISA profiling and manufacturing QC data on-demand.

As a Data Engineer, you will play a critical, first-hand role in developing core improvements to the data pipelines and data infrastructure for handling all things nELISA data. In particular:

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You will primarily be responsible for designing, building, iteratively improving, automating, deploying and scaling our data pipelines for processing flow cytometry data into quantitative protein measurements. This will be done in close collaboration with your Data Engineering and Software Engineering teammates.

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You will support or lead the design and implementation of our data platform architecture, including data lakes and related infrastructure, and build and maintain data pipelines to extract, transform, and load (ETL) data from various sources into the data storage systems, ensuring data quality, reliability, and scalability.