We're on a mission to help people discover the real value of travel — to inspire, to give more reasons, to make it easy — for you to go. Our company was founded back in 2005, and since then, we've imagined and created some of the most well-loved products for travelers all around the world.

Today, Wego is used by millions of people every month — people who travel for adventure, work, family, and for many other reasons. That's why we work tirelessly to make your experience of planning & booking flights, hotels, and trips as seamless as possible.

Team Description:

You'll join Wego's Data Analytics & Data Science team, which powers analytics, data science and AI tooling across our Flights and Hotels products, from the metasearch marketplace to our OTA business, in markets worldwide.

Our roadmap has more high-impact projects than ever, and we're growing the team to deliver the data ingestion, platform and AI tooling work behind them.

The Role:

You will build and operate the ingestion systems and runtime infrastructure that support analytics, data science and AI tools across Wego. Your focus is reliable data movement from source systems and dependable operation of the services that consume it, and you will own pipelines and services through development, deployment and operation.

You'll work closely with analysts, data scientists and engineering teams to find bottlenecks and build practical solutions, and you'll connect AI tools to data and services with clear access boundaries, evaluations and operational ownership.

What you will be working on:

Building reliable batch and streaming pipelines so data moves from source systems without being silently lost or duplicated
Running pipelines and services on Kubernetes and cloud infrastructure, with repeatable environments and safe release and rollback paths
Building the systems that let AI assistants and agents use Wego data and APIs reliably, with evaluations built alongside data scientists
Improving reliability and cloud cost against agreed service goals
Finding sources of data loss, operational toil and slow delivery, validating their impact with users and owning the fix end to end

Core Responsibilities:

Data ingestion: Build pipelines from APIs, databases and event sources; handle schema changes, incremental loads and change data capture where needed
Recovery & data quality: Design retries, deduplication, backfills and replay; monitor freshness and completeness from source to consumer
Kubernetes & runtime: Deploy and operate containerized pipelines and services; manage resources, configuration, scaling and recovery with platform teams
DevOps & delivery: Use infrastructure as code and automated delivery pipelines; test changes and manage secrets and access
Observability & operations: Define metrics, logs and alerts; diagnose incidents and fix recurring failures
AI tooling & model services: Build tool and API integrations, agent loops, context and memory, retrieval, prompts and guardrails; support model serving where needed
Evaluations: Build test sets, regression checks and monitoring of AI outputs with data scientists

Skills and experience:

3–8 years of experience in data engineering, platform engineering or related infrastructure work
Experience owning ingestion systems in production, including failures, recovery and source schema changes
Hands-on experience operating Kubernetes workloads and cloud infrastructure
Experience with DevOps practices, automated deployments and infrastructure as code
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 delivery guarantees, idempotency and schema compatibility, and how they affect downstream data
Experience with Kubernetes and container tooling such as Docker and Helm
Experience with a cloud platform such as Google Cloud Platform or AWS, and infrastructure tools such as Terraform
Practical knowledge of CI/CD, monitoring, alerting and incident diagnosis
Ability to build AI assistants and agents (tool calling, agent loops, context and memory, retrieval, guardrails) and evaluate them with test sets and regression checks
You spot worthwhile problems without waiting to be assigned, validate why they matter and own the solution through to measurable impact
You align priorities with stakeholders, then work independently through ambiguity and missing context
You use AI fluently but own the output: you test generated code, investigate failure paths and own what you deploy
You provide tests and operational evidence before calling a change complete, and fix defects after release
You explain engineering tradeoffs in practical terms and treat analysts, data scientists and product teams as users of what you build

Additional Info:

Experience building AI assistants, agents, evaluations or model services is a plus
Travel-domain experience is a plus

Data & AI Platform Engineer