About Cypris:
At Cypris, we're building the single ecosystem for global innovation data. Cypris is an AI-powered research tool that centralizes data sources like scientific papers, global patents, market news, and company data into one platform. Cypris arms users with access to unique insights from 500M+ global data points, answering key questions about their market, competitors, core technologies & more to support new product development, commercial strategy & accelerate global innovation.
We’re connecting R&D teams to the global innovation landscape like the Bloomberg Terminal did for the finance world & Pitchbook for venture capital. Current users include leading R&D & innovation teams at mid-size to Fortune 100 companies operating in emerging markets like aerospace, genomics, cancer research, autonomous vehicles, and more.
About the Role:
In this role, you will be responsible for designing, building, and maintaining scalable data pipelines and systems to support our data-driven platform. You will work closely with other members of our engineering team to ensure the availability and quality of data required to supply innovation analytics and insights. This is an exciting opportunity to contribute to our data infrastructure and help shape the future of our data capabilities.
In This Role You Will:
- Design, develop, and optimize robust data pipelines to process and transform large datasets from various sources
- Optimize data stores' performance in terms of index response times and query response times
- Implement and maintain ETL processes to ensure data accuracy and integrity
- Collaborate with cross-functional teams to understand data requirements and deliver effective data solutions
- Develop and maintain data warehouses and data lakes to support business intelligence and analytics
- Monitor and troubleshoot data pipeline performance and reliability, implementing improvements as needed
- Ensure data security and compliance with relevant regulations and standards
- Stay up-to-date with the latest technologies and best practices in data engineering and incorporate them into our processes
A Key Candidate Will Have:
- 7+ years of proven experience as a Data Engineer or in a similar role
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- Proficiency in programming languages like Python, Java, or Scala
- Experience with cloud platforms such as GCP (preferred), AWS, Google Cloud, or Azure
- Hands-on experience with big data technologies such as Hadoop, Spark, or similar frameworks
- Knowledge of data warehousing concepts and experience with tools like Redshift, BigQuery, or Snowflake
- Familiarity with ETL tools and processes
- Strong problem-solving skills and attention to detail
- The desire to contribute and grow at an early-stage startup
Technologies We Use:
- Python
- GCP
- Apache Beam
- MongoDB
- Elasticsearch
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