Senior Software Engineer - Full Stack
Ampere- Full Time
- Senior (5 to 8 years)
Candidates should have 4-6+ years of industry experience as a software engineer, preferably in a growth engineering capacity. A fundamental understanding of core technologies such as React (TypeScript), Python (Django), Postgres, and AWS is essential. Proficiency in communicating complex technical issues to both technical and non-technical audiences is required, along with a supportive attitude and the ability to autonomously solve challenges. Candidates should also demonstrate a commitment to best practices in engineering, security, and design.
The Senior Full-stack Software Engineer will collaborate with designers and product managers to enhance the user onboarding experience. They will conduct quantitative and qualitative analysis to identify opportunities for improving the product led growth funnel. The engineer will work with the engineering team to enhance the scalability of Secoda's platform and assist the customer success team in diagnosing and resolving bugs. Additionally, they will guide the product team in developing features that address key customer challenges.
Data management and enablement platform
Secoda provides a platform for data management and enablement, helping organizations track, document, and discover their data efficiently. The platform is especially useful for businesses that deal with large amounts of data, ensuring that it is accessible, understandable, and trustworthy. Key features include data lineage tracking, which shows the origin and flow of data, and comprehensive data documentation tools that help create data dictionaries and metadata repositories. This functionality enhances transparency and trust in the data used by teams. Secoda serves a wide range of clients, from small startups to large enterprises, and operates on a subscription model, allowing clients to access various features based on their needs. The goal of Secoda is to provide a single source of truth for data, improving collaboration and productivity within data-centric organizations.