No experience
Employment Type:
Full time
Job Category:
Software Development
Test Engineer
(This job is no longer available)
Alpine Data Labs | San Francisco, CA
Grad Date

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Job Description

Test Engineering Positions for Aspiring Data Scientists

Develop deep skills in data science as a Test Engineer at Alpine Data Labs, "one of the hottest Hadoop startups", according to Business Insider, Datamation, and the Wall Street Journal. Alpine recently gained $16M in funding to build "the next big data startup" (Forbes).

The Role

Are you interested in learning about analytics, data science, and big data? This is an opportunity for anyone with a technical background to get involved in the world of data science and application development by helping to build out an automated test suite using a new test framework for our predictive analytics and machine learning platform. Work alongside a high-powered team of engineers and data scientists to test accuracy, scalability and usability, and help dream up new capabilities for Alpine's applications so that we can bring data science to the broadest possible audience.

The Company

The vision of Alpine Data Labs is to make data science so straightforward that it becomes a tool for business stakeholders as well as data scientists. Alpine's data-mining software scales to huge datasets (hundreds of terabytes, or billions of rows) but it is driven through an intuitive graphical interface. This well-positioned startup delivers on the promise of Hadoop and Big Data by providing a collaborative and intuitive visual environment for teams to quickly create and deploy analytics workflows and predictive models.

We are a well-funded, rapidly-growing startup backed by major venture firms. Our customers include Barclays, Sony, Kaiser Permanente, and the US Federal Reserve.


Learn the Alpine Web Application as quickly as possible along with the test methods used by Test Engineers at Alpine to detect and report defects

Build models and workflows to test that Alpine's predictive analytic functionality performs as expected against Big Data

Conduct deep testing and trouble-shooting during the course of test execution

Work closely with Architects, Developers, and PM to write test cases and report issues

Perform tests using widely practiced black box testing techniques such as boundary value analysis and exhibit growth in your career as a test engineer by learning how to effectively use tools such as Chrome's developer tools to view REST requests and responses

Exhibit an eagerness to learn and grow in your career

Provide detailed defect reports by attaching logs with relevant errors or exceptions resulting from your tests along with screenshots, cogent descriptions and steps to reproduce along with actual vs. expected results

Be the customer advocate and voice of quality across the development organization


College grad with internship experience or up to 2 years testing Web Applications or Big Data applications

Some familiarity with techniques such as machine learning, statistical analysis and predictive modeling to design & deliver effective tests, OR a high level understanding of the following technologies and how they are used in the business world: Hadoop, Databases, Analytics, Application Servers such as Tomcat, LDAP Servers

A basic working knowledge of the Linux OS and can at a minimum traverse the OS, perform file manipulations, and learn from self-directed study in addition to learning from peers or academic course-work

BS/MS in Computer Science, Mathematics, Machine Learning or Data Science or a related field

Bonus Points:

An interest in machine learning and predictive analytics

Software Development experience

Programming experience in the context of creating automated test scripts, writing applications in a robust language such as Java, Scala or a scripting language such as JavaScript, or shell scripts


Stock options

Great benefits

Quality snacks

Friday beer o'clock

Regular social outings


A. We are, apparently, hot:






B. You can find lots of useful information on our web-site. In particular, check out the product info here:


C. Here's an interview we did that describes the business motivation behind what we do:


D. Here's examples of the press coverage that Alpine is getting:


E. And finally, here's some of the academic and open-source background to what Alpine does:



About Alpine Data Labs

Alpine Data Labs was founded in 2010 and has a rich history anchored in Data Science and Predictive Analytics that considerably predates the official launch of the company. Steven Hillion, as a leader of data science and engineering groups for fifteen years, had pioneered a new business model of delivering Data Science consultation services that leveraged open-source analytics tools as well as high-performance computing infrastructure. While these projects cut across several industries and business models, including marketing mix modeling for consumer-packaged goods and product recommendations for retail banking, some common themes were emerging: a need for scalability, coupled with advanced analytics, and the importance of collaboration across the disparate teams that work with data. Drawing on this experience during his time at Greenplum, Steven led the development of an accessible, analytic framework for Data Science that provided a collaborative interface around data acquisition and exploration, sandbox provisioning, as well as visualization and data mining. That analytics productivity framework remains a critical part of the analytics offerings for Alpine. At the same time, Scott Yara (president and co-founder of Greenplum) and Greenplum innovators Anderson Wong and Yi-Ling Chen saw an opportunity to provide greater access to all the analytical power of MPP databases by exposing them in a Data Science application that simplified the construction of end-to-end analytics workflows. Scott provided seed funding for the development of a prototype for this analytics application framework, and Anderson and Yi-Ling co-founded Alpine with a small team of engineers to create what is now the in-database foundation of the Alpine application, building on requirements from Steven and his team. The demand for scalable analytics existed beyond Greenplum and beyond relational databases, so within a short time Alpine had a roadmap that expanded the offering to other databases and to Hadoop. In 2011 the company had secured Series A funding. By early 2012 the company was delivering its application to organizations around the world, and Steven Hillion joined full-time as Chief Product Officer.