Data Engineer - TMD

Sequoia Connect

9h • Hybrid

About The Position

Our client represents the connected world, offering innovative and customer-centric information technology experiences, enabling Enterprises, Associates, and Society to Rise™. They are a USD 6 billion company with 163,000+ professionals across 90 countries, helping 1279 global customers, including Fortune 500 companies. They focus on leveraging next-generation technologies, including 5G, Blockchain, Metaverse, Quantum Computing, Cybersecurity, Artificial Intelligence, and more, on enabling end-to-end digital transformation for global customers. Our client is one of the fastest-growing brands and among the top 7 IT service providers globally. Our client has consistently emerged as a leader in sustainability and is recognized amongst the ‘2021 Global 100 Most sustainable corporations in the World by Corporate Knights. We are currently searching for a Data Engineer Responsibilities Infrastructure as Code (IaC): Architect and maintain GCP data infrastructure using Terraform to ensure consistency across environments (Dev, Stage, Prod). Lakehouse Architecture: Design enterprise-grade storage and compute layers using BigQuery, GCS, and Dataproc. Automated Provisioning: Develop CI/CD pipelines for automated deployment of data resources (Pub/Sub topics, Dataflow jobs, Cloud Composer environments). Data Modeling: Create canonical and domain-specific data models to support AI/ML and operational products. Cross-Cloud Connectivity: Implement BigQuery Omni to enable federated queries across different cloud providers without moving data. Self-Service Enablement: Build federated data layers that allow downstream consumers to access data products autonomously. Requirements GCP Data Engineer with Terraform, IaC Architect and design an enterprise grade GCP based data lakehouse leveraging BigQuery, GCS, Dataproc, Dataflow, Pub/Sub, Cloud Composer, and BigQuery Omni. Define data ingestion, hydration, curation, processing and enrichment strategies for large scale structured, semi structured, and unstructured datasets. Create data domain models, canonical models, and consumption ready datasets for analytics, AI/ML, and operational data products. Design federated data layers and self service data products for downstream consumers. Must Terraform (Mandatory) Languages Advanced Oral English. Native Spanish. Note: On-site | Hybrid | Fully remote | Temporarily remote due to COVID-19 | Work-from-home flexibility. <-- Select only one If you meet these qualifications and are pursuing new challenges, Start your application to join an award-winning employer. Explore all our job openings | Sequoia Career’s Page: https://www.sequoia-connect.com/careers/ . GCP Data Engineer with Terraform, IaC Architect and design an enterprise grade GCP based data lakehouse leveraging BigQuery, GCS, Dataproc, Dataflow, Pub/Sub, Cloud Composer, and BigQuery Omni. Define data ingestion, hydration, curation, processing and enrichment strategies for large scale structured, semi structured, and unstructured datasets. Create data domain models, canonical models, and consumption ready datasets for analytics, AI/ML, and operational data products. Design federated data layers and self service data products for downstream consumers.

Requirements

• GCP Data Engineer with Terraform, IaC Architect and design an enterprise grade GCP based data lakehouse leveraging BigQuery, GCS, Dataproc, Dataflow, Pub/Sub, Cloud Composer, and BigQuery Omni.
• Define data ingestion, hydration, curation, processing and enrichment strategies for large scale structured, semi structured, and unstructured datasets.
• Create data domain models, canonical models, and consumption ready datasets for analytics, AI/ML, and operational data products.
• Design federated data layers and self service data products for downstream consumers.
• Must Terraform (Mandatory)
• Languages Advanced Oral English.
• Native Spanish.

Responsibilities

• Infrastructure as Code (IaC): Architect and maintain GCP data infrastructure using Terraform to ensure consistency across environments (Dev, Stage, Prod).
• Lakehouse Architecture: Design enterprise-grade storage and compute layers using BigQuery, GCS, and Dataproc.
• Automated Provisioning: Develop CI/CD pipelines for automated deployment of data resources (Pub/Sub topics, Dataflow jobs, Cloud Composer environments).
• Data Modeling: Create canonical and domain-specific data models to support AI/ML and operational products.
• Cross-Cloud Connectivity: Implement BigQuery Omni to enable federated queries across different cloud providers without moving data.
• Self-Service Enablement: Build federated data layers that allow downstream consumers to access data products autonomously.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed