About The Company

WalletConnect is the open-source web3 standard to connect blockchain wallets to dapps. Any wallet, any dapp, any chain. Starting in 2018, our mission is to make web3 accessible to everyone. Every month, millions of people use WalletConnect in thousands of integrations. We raised ~$25M from venture investors including 1kx and Coinbase as well as from customers such as Shopify and Circle. We are growing fast both in terms of features and users.

To learn more about our plans to create a multi-API messaging network for web3, take a look at our presentation at EthCC.

Job Short Description

The Role:

We are looking for someone who can help mature our existing data applications and innovate new use cases. Currently we have many different sensors and pipelines to expose their data in data lake/data warehouse. We run BI queries on top of that data and have Spark jobs into RDS to expose them to specific use-cases.

We have some data science applications as well. There’s many obvious machine learning use-cases that we also haven’t yet explored but would like to.

We’re looking for someone who can act as a leader in our product team and help build and fulfill the vision.

Responsibilities:

• Design and implement end-to-end data pipelines, and work closely with stakeholders to build instrumentation and define data models
• Take ownership of monitoring to ensure high availability of our data tooling
• Build actionable production-quality internal dashboards, with well defined KPI’s
• Work with the Cloud team to create customer-focused dashboards to help customers understand their WalletConnect usage.
• Partner closely with product, engineering and other business stakeholders to influence product and business decisions with data
• Influence leadership to drive more data-informed decisions

Requirements

Must have:

• 3+ years of experience as a Data engineer/scientist.
• Extensive experience with Python/Spark/databases
• Deep understanding of advanced SQL techniques, dimensional modeling and scaling ETL pipelines
• Experience with applied statistics and quantitative modeling
• Demonstrated knowledge in monitoring data systems