Seed solves big problems with big data. We architect data-driven, technology-based solutions to a wide range of complex problems for global clients in the commercial, public, and social sectors — from music to global development, automotive to consumer goods. Our vision is to build an era-defining global consultancy with data at its core.
Seed Scientific is looking to hire an Associate Data Engineer to join its team in New York. You will collaborate with Data Engineers, Data Scientists and Design Technologists to build custom, data-driven tools to help our clients in the commercial, public, and social sectors solve a wide range of complex human-scale problems spanning global development, music, health, cybersecurity, etc. The Data Wrangler will access, organize and prepare data to power the tools we build for our clients. The candidate must be self-directed and enjoy working in a fast-paced, dynamic, collaborative environment.
- Fluency with Python
- Fluency with SQL
- Ability to wrangle and process large data files into usable formats and databases
- Enthusiasm for learning new techniques and technologies to solve hard problems
- Collaborate with team members to ensure appropriate data is identified, sourced, collected, and made available for each analytic and reporting project.
- Write testable scripts to automate data processing
- Collaborate with team members to ensure data integrity and security compliance are maintained for all analytic and reporting projects.
- Construct data files from large databases; extract, transfer, merge, organize, clean, format and store data for analysis and reporting.
- Troubleshoot and perform data audits to ensure and improve data integrity; investigate and resolve data discrepancies.
- A degree in Computer Science
- Web/API building
- Familiarity with Git for version control
At Seed, you will be joining an interdisciplinary group of scientists, mathematicians, designers, and engineers that work with commercial, public and social sector clients to help them solve their most challenging problems and get the most out of their data. To learn more, visit seedscientific.com.