Description
At Sequoia Connect, we are a Talent-First Technology Ecosystem that redefines how elite professionals interact with the general digital landscape. We move beyond traditional models to act as a catalyst for the top 1% of global talent, connecting human potential with complex industrial execution. By joining our inner circle, you are not simply taking a position; you are aligning with a strategic partner dedicated to updating your "Human OS" and accelerating your growth through world-class, high-impact projects.
We are currently partnering with a rapidly growing, automation-led powerhouse that serves 31 Fortune 500 companies across the financial, healthcare, and manufacturing sectors. With a global workforce of over 32,000 employees and a presence in 28 countries, our client is a titan of digital transformation. Their "Automate Everything, Cloudify Everything" strategy ensures you will be working at the absolute forefront of AI-driven automation and cloud solutions.
This is your chance to thrive in a "Customer Success,



First and Always" environment that prizes continuous learning and radical ownership. You will collaborate within an international network of expertise across 39 delivery centers worldwide, gaining exposure to complex engineering challenges that redefine industrial standards. If you are a driven professional looking for a dynamic, forward-thinking workplace where your growth is the priority, this is where you belong.
We are currently searching for an AWS Data Engineer:
The Challenge (Responsibilities)
- Design, develop, and maintain automated ETL pipelines using Python and Apache Airflow.
- Orchestrate different pipelines to run in sequence or parallel, tracking job pipeline runs to reprocess error records.
- Troubleshoot data pipeline errors and proactively fix issues.
- Export and Import data to/from various formats (CSV, JSON, XML) primarily from S3 or other cloud storage solutions.
- Optimize complex SQL queries for performance within data

📌 AWS Data Eng (México)
🏢 Sequoia Connect
📍 México