Senior Data Integration Engineer (Python, APIs, ETL)

Talently; New York (NY); On-site; MXN 2,216,000 - 3,323,000; Full time

Job summary

QuadSci, a New York-based AI startup, seeks a Forward Deployed Engineer to lead customer integrations from data discovery to production deployment. You will design and maintain data pipelines, build Python-based processing and orchestration systems, and expose platform functionality via REST APIs.

The role emphasizes performance, memory efficiency, and strong collaboration with customer technical teams to understand diverse data sources and system constraints.

Qualifications

• 5+ years building production software.
• Strong Python production experience.
• Hands-on experience with ETL and data pipelines.
• Solid understanding of REST APIs, OAuth/API-key auth, webhooks, and asynchronous background jobs.
• Proven track record of integrating with third-party SaaS platforms.

Responsibilities

• Collaborate with customer technical teams to understand data sources, schemas, and system constraints.
• Own the technical implementation for customer integrations from initial data discovery to production deployment.
• Design and maintain pipelines to ingest, clean, and transform data from diverse sources (APIs, Databases, Events).
• Build Python-based systems for data processing and orchestration, focusing on performance and memory efficiency.
• Develop and maintain REST APIs, services, and workers that expose platform functionality.
• Create shared libraries and internal tooling to support scalable data flows and improve the developer experience.

Skills

Python

ETL pipelines

REST APIs

OAuth / API keys

Asynchronous processing

SaaS integrations

Job description

QuadSci, a New York-based AI startup, seeks a Forward Deployed Engineer to lead customer integrations from data discovery to production deployment. You will design and maintain data pipelines, build Python-based processing and orchestration systems, and expose platform functionality via REST APIs.

The role emphasizes performance, memory efficiency, and strong collaboration with customer technical teams to understand diverse data sources and system constraints.