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← All postings · June 2018 thread

Snowflake Computing

CompanySnowflake Computing
Websitesnowflake.net
Typefull-time
Role taxonomyData / Analytics
SpecialtiesData Engineering, Data Science
LocationSan Mateo, CA
Salary
Apply viaApplication linkhttps://www.snowflake.net/about/careers/#open-positions
Hiring notes
TechPythonJavaRubyScalaDockerAWS
Parsed locationsSan Mateo, CA
Posted bysnowflake_data
PostedJun 3, 2018
SourceView on Hacker News ↗

Original posting

Snowflake Computing | San Mateo, CA (ONSITE) | Full-time | https://www.snowflake.net/about/careers/#open-positions Snowflake is the data warehouse built entirely for the cloud. Our Data and Analytics team is hiring two positions: 1. Data Engineer: https://jobs.lever.co/snowflake/352fc68f-e825-47cd-9f2f-d796... We're looking for an experienced Data Engineer who has built production-grade, large-scale data pipelines. The person will be the first dedicated Data Engineer on our team, and have an opportunity to architect and implement a number of pipelines into our data warehouse (Snowflake, naturally), as well as pipelines from the warehouse into core business systems. 2. Data Scientist: https://jobs.lever.co/snowflake/ca490b99-54c7-4041-9a0d-7edf... We're look for a seasoned Data Scientist who has real-world experience building and deploying models in mission-critical production settings. The lion's share of our near-term projects are unsupervised or semi-supervised learning problems; we also have a lot of time-series analysis projects. We have a lot of interesting use cases around operational optimization—e.g., anomaly detection, forecasting server demand, adding intelligence to the automation of server provisioning, etc. There are opportunities for customer-facing features as well. Languages we like: SQL, Python, R, Java, Scala, and Ruby. Tools we like: Snowflake, Airflow, Docker, Spark, AWS Lambda, Alooma, Fivetran, and Looker. The interview process: 1. Review application. 2. 30-minute conversation with hiring manager (Director of Data and Analytics => https://www.linkedin.com/in/scottdhoover/). 3. 45-minute SQL coding session over the phone (we're a database company—strong SQL and RDBMS understanding is essential). 4. Take-home coding project (either example pipeline for Data Eng. or analysis for Data Sci.). Should take no more than a couple of hours. 5. On-site with representatives from the Data team, Engineering, DevOps, and Product.