Data & AI Platform Engineer

Wego; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Wego is seeking a data engineer to build and operate data ingestion systems and runtime services that support analytics, data science, and AI tools across flight and hotel products. You’ll own pipelines from development through operation, helping teams move data reliably and run workloads dependably.

The role also includes connecting AI assistants and agents to data and APIs with controlled access and meaningful evaluations.

Qualifications

• 3–8 years in data engineering or platform engineering.
• Own ingestion systems with failure recovery and schema changes.
• Hands-on Kubernetes and cloud operations, IaC practices.
• Fluency in Python and SQL, practical Linux and networking.
• Experience with APIs, databases, and event systems (batch/streaming).
• Familiarity with Docker, Helm, cloud platforms (AWS/GCP) and Terraform.

Responsibilities

• Build batch and streaming ingestion from APIs, databases and events.
• Design retries, deduplication, backfills and replay; monitor data freshness.
• Deploy and operate containerized pipelines on Kubernetes and cloud.
• Automate delivery, manage configuration, access, and safe release paths.
• Define monitoring, logs and alerts; investigate incidents and fixes.
• Integrate AI tools with data and services; build evaluations with data scientists.

Skills

Python

SQL

Linux

Networking

Containerization

CI/CD

Cloud platforms

Incident diagnosis

Tools

Docker

Helm

Terraform

CI/CD tooling

Job description

Role overview

Build and operate the data ingestion systems and runtime services that support analytics, data science, and AI tools across flight and hotel products. You’ll own pipelines and services from development through operation, helping teams move data reliably and run workloads dependably. The role also includes connecting AI assistants and agents to data and APIs with controlled access and meaningful evaluations.

Responsibilities

• Build batch and streaming ingestion from APIs, databases, and event sources, accounting for schema changes and incremental loads.
• Design retries, deduplication, backfills, and replay; monitor data freshness and completeness.
• Deploy and operate containerized pipelines and services on Kubernetes and cloud infrastructure.
• Automate delivery and infrastructure, manage configuration and access, and create safe release and recovery paths.
• Define monitoring, logs, and alerts; investigate incidents and fix recurring failures.
• Integrate AI tools with data and services, and build evaluations and regression checks with data scientists.

Requirements

• Three to eight years in data engineering, platform engineering, or related infrastructure work.
• Production experience owning ingestion systems, including failure recovery and source schema changes.
• Hands-on Kubernetes and cloud operations, plus automated deployment and infrastructure-as-code practices.
• Fluency in Python and SQL, with practical Linux, networking, and container skills.
• Experience with APIs, databases, and event systems, including batch or streaming ingestion; understanding of idempotency, delivery guarantees, and schema compatibility.
• Familiarity with Docker, Helm, a cloud platform such as Google Cloud or AWS, and infrastructure tools such as Terraform.
• Practical CI/CD, monitoring, alerting, and incident diagnosis skills; ability to build and evaluate AI assistants or agents.

Nice to have

• Experience building AI assistants, agents, evaluation systems, or model services, or familiarity with the travel domain.