Data Infrastructure Specialist
1 month ago
About Chemify
Chemify Ltd is driving innovation in the field of chemistry, aiming to improve quality of life for patients and extend the reach of humanity. Our core focus is on building the infrastructure to digitize chemistry, designing, making, and discovering new molecules with optimal properties instantly and on-demand.
We have developed a platform combining purpose-built programming languages, robotics, AI, and the world's largest and continuously growing database of chemical programs to accelerate chemical discovery. Our data platform underpins end-to-end design, planning, execution, monitoring, reporting, and learning from chemical synthesis campaigns.
Job Requirements
We are seeking a talented Data Engineer to enable the next iteration of our data platform, supporting our innovative automated chemistry platform. You'll work in a growing, cross-functional engineering team with expertise in software, hardware, data engineering, AI research, and infrastructure.
Key Responsibilities:
- Design and implement scalable, cost-effective, and observable data infrastructure to support key research and commercial objectives.
- Collaborate with the ML Research group to construct high-quality, traceable, and scalable data products for chemistry automation use cases.
- Own company-wide business intelligence solutions for democratising data visualisation and analysis.
- Develop a data catalog solution to manage the lifecycle of acquiring and leveraging data sets for highest impact use cases.
- Work with the Infrastructure team to ensure the data platform is automated and well-architected.
- Collaborate with the software team to ensure data modelling within applications follows best practices.
- Stay up-to-date with industry trends and improve internal data architecture and processes.
Requirements
4+ years' experience in Data Engineering, preferably for B2B or B2C commercial products leveraging AI & ML. Proficiency with Python and extensive experience with databases such as Postgres & DynamoDB. Expertise in data pipelining and familiarity with modern Data platforms such as Databricks and Snowflake. Knowledge of leveraging AWS services to build applications. Knowledge of business intelligence solutions for rapid turnaround of critical data questions.
Familiarity with continuous integration/continuous deployment and tools such as Gitlab and ArgoCD. Ability to assimilate complex requirements from stakeholders into actionable tasks within rapid agile iterations. Ability to work hybrid from our Glasgow-based office. Beneficial Skills: Experience within AI, Life Sciences, Manufacturing, or Laboratory Automation domains. Interest in data science, particularly NLP and general ML engineering. Interest in ontologies, data modelling, and semantic technologies. Contribution to open-source projects or the agile community. Interest in Mechanical engineering and robotics.
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