Job description
Description
At Sequoia Connect, we are a Talent-First Technology Ecosystem that redefines how elite professionals interact with the global 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 a Junior Data Engineer:
The Challenge (Responsibilities)
Assist in building and maintaining ETL pipelines using Python and PySpark.
Support the development of workflows utilizing AWS Glue, Lambda, and Step Functions.
Work extensively with cloud data storage platforms, including S3, Redshift, RDS, and Oracle.
Write complex SQL queries for data extraction, transformation, validation, and reporting.
Help implement basic monitoring, logging, and error handling for data pipelines.
Support the ingestion and processing of data from APIs and JSON payloads.
Collaborate with software engineers, data analysts, and business stakeholders to understand requirements.
Contribute to code management, technical documentation, and deployment support activities.
Your Profile (Requirements)
Degree holders for the visa application process.
0 to 4 years of software development or data engineering experience across relevant cloud platforms.
Good knowledge of Python and SQL.
Solid understanding of AWS services, specifically S3, SNS/SQS, EMR, Glue, Lambda, Redshift, and Step Functions.
Strong grasp of ETL concepts and data processing fundamentals.
Familiarity with GitLab, Terraform, and the Software Development Life Cycle (SDLC) from development to production.
Familiarity with PySpark, AWS managed services, data engineering best practices, and code optimization.
High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery.
Technologist DNA: A deep understanding of the difference between "coding" and "engineering."
Desired
Exposure to PySpark, Athena, CloudWatch, SNS, and SQS.
Internship, project, or academic experience specifically in cloud computing, analytics, or data engineering.
Familiarity with cloud-native foundations or AI coding assistants (e.g., GitHub Copilot).
Languages
Advanced Oral English: For seamless collaboration with global teams.
Advanced Spanish.
Special Notes
Our Client is seeking candidates focused on foundational skill-building in a dynamic cloud environment.
Work Arrangement
We value flexibility to support your lifestyle. This position is available as:
Remote
If you meet these qualifications and are pursuing new challenges, start your application on our website to join an award-winning employer. Explore all our job openings | Sequoia Career’s Page:
Requirements
0-4 years of software development experience across the appropriate platform.
Good knowledge on Python and SQL.
Good understanding to AWS services such as S3, SNS/SQS, EMR, Glue, Lambda, Redshift, and Step Functions.
Good understanding of ETL concepts and data processing fundamentals.
Familiarity with GitLab/Terraform and SDLC from development to production.
Familiarity to PySpark, AWS managed services, data engineering best practices, and code optimization.
Good analytical, problem-solving, and communication skills.
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.