Data Cloud Engineer (DataOps)

Confidential

Remote job permanent

Posted: January 30, 2026

Required Skills

Job Description

About Trafilea

Trafilea is a Consumer Tech Platform for Transformative Brand Growth. We’re building the AI Growth Engine that powers the next generation of consumer brands.

With over $1B+ in cumulative revenue, 12M+ customers, and 500+ talents across 19 countries, we combine technology, growth marketing, and operational excellence to scale purpose-driven, digitally native brands.

We own and operate our own digitally native brands (not an agency), with presence in Walmart, Nordstrom, and Amazon, and a strong global D2C footprint.

Why Trafilea

We’re a tech-led eCommerce group scaling our own globally loved DTC brands, while helping ambitious talent grow just as fast.

🚀 We build and scale our own brands.

🦾 We invest in AI and automation like few others in eCom.

📈 We test fast, grow fast, and help you do the same.

🤝 Be part of a dynamic, diverse, and talented global team.

🌍 100% Remote, USD competitive salary, paid time off, and more.

Key Responsibilities

The mission of the Data Cloud Engineer is to design, implement, and maintain scalable, secure, and cost-effective cloud infrastructure and data pipelines on AWS. This role combines DevOps and DataOps practices to ensure seamless infrastructure automation, data management, and deployment workflows. The engineer will collaborate cross-functionally with data, BI, and development teams to support critical business needs while proactively identifying improvements in automation, security, and cost efficiency.

• Design, deploy, and optimize AWS cloud infrastructure (S3, Redshift, Lambda, ECS) for scalability and performance.

• Build and maintain data architectures and ETL pipelines supporting BI and analytics.

• Implement Infrastructure as Code (Terraform, Terragrunt) and manage CI/CD pipelines for automation.

• Use Docker and Kubernetes to enable containerized, reliable deployments.

• Ensure cloud security, compliance, and cost efficiency across environments.

• Collaborate with engineering and data teams to resolve issues and enhance workflows.

• Document best practices and share expertise to strengthen the team’s capabilities.

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