Data Engineer II
Scientific
Posted on 9/27/2023
INACTIVE
TetraScience

51-200 employees

R&D data cloud management
Company Overview
Tetra's mission is to combine deep domain knowledge, the industry's only purpose-built scientific Data Cloud, and the largest network of life sciences innovators, to harness the power of the worlds scientific data.
Data & Analytics

Company Stage

Private

Total Funding

$99.1M

Founded

2019

Headquarters

Boston, Massachusetts

Growth & Insights
Headcount

6 month growth

-9%

1 year growth

-6%

2 year growth

19%
Locations
Cambridge, MA, USA • Remote in USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
SQL
Python
CategoriesNew
Data & Analytics
Requirements
  • 4+ years in Python and SQL
  • Passionate about science and building solutions to make the data more accessible to the end-users
  • Undergraduate or graduate degree in chemistry, biology, computer science, statistics, public health, etc
  • Wet lab experience or experience with scientific instruments is a strong plus
  • Excellent communications skills, attention to details, and the confidence to take control of project delivery
  • Quickly understand a highly technical product and effectively communicate with product management and engineering
  • Strong problem-solving skills
  • Intellectually curious: Unwavering drive to learn and know more every day
  • Ability to think creatively on how to solve projects risks without reducing quality
  • Team player and ability to "roll up your sleeves" and do what it takes to make the team successful
Responsibilities
  • Own, prototype, and implement customer solutions
  • Research and prototype data acquisition strategy for scientific lab instrumentation
  • Research and prototype file parsers for instrument output files (.xlsx, .pdf, .txt, .raw, .fid, many other vendor binaries)
  • Design and build data models
  • Design and build Python data pipelines, unit tests, integration tests, and utility functions
  • Facilitate internal project post-mortems to identify areas of improvement on the next implementation