Data Architect (Principal SWE) at Isomorphic Labs | Torre
Data Architect (Principal SWE)
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Isomorphic Labs

Data Architect (Principal SWE)

Isomorphic Labs
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Full-time

Legal agreement: To be defined

USD75.4K - 100K/year

~COP150M - 200M/year

+ Equity

+ Bonuses

location_on
Hybrid (Lausanne)
Posted almost 3 years ago

Responsibilities

Isomorphic Labs is a new Alphabet company that is reimagining drug discovery through a computational- and AI-first approach. We are on a mission to accelerate the speed, increase the efficacy and lower the cost of drug discovery. Come and be part of a multi-disciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards achieving ambitious goals, while being a part of an inspiring, collaborative and entrepreneurial culture. We are looking for a data architect to join our data engineering team at Isomorphic Labs in Lausanne. This is an exciting opportunity to work on a greenfield ML-based software platform that will transform the biopharmaceutical world. As a data architect, you will play a central hands-on role in designing and building Isomorphic Labs’ cloud-based data platform, implementing data governance solutions, and collaborating with interdisciplinary stakeholders to create and maintain data models. If you have experience with data intensive systems in a machine learning context, building secure/scalable platforms on cloud, and strong programming skills, we would love to hear from you. Responsibilities: - Play a central hands-on role in designing and building Isomorphic Labs’ cloud-based data platform. - Define and implement data governance solutions to ensure data freshness, quality, consistency, discoverability, security and compliance. - Design the architecture and write software for the platform’s components. - Design and deploy solutions for dataset version control and lineage tracking. - Contribute to developing a data lake / lakehouse, centralizing some of Isomorphic Labs’ main data assets, and making them available for consumption by machine learning workflows. - Work with interdisciplinary stakeholders to create and maintain data models. - Provide documentation, guidance and communication on the data platform to the wider organization. - Partner and collaborate with a diverse set of teams incl. science, ML research, product, business development and operations.
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