Sr Data Engineer

You’ll take ownership of a large-scale AWS data platform powering analytics for thousands of hotels and restaurants worldwide. This is a hands-on role where your work directly impacts business decisions across the hospitality industry — not internal dashboards nobody uses.

We’re looking for someone who doesn’t just build pipelines — but runs them, fixes them, and makes them bulletproof.

About the Product

A hospitality technology company operating a data analytics platform serving:

• 2,500+ hotels
• 500+ restaurants

The system processes operational and performance data, delivering insights to product and analytics teams who rely on it daily.

Your Mission

Own and operate the AWS data infrastructure:

• Build scalable, production-grade data pipelines
• Ensure reliability, performance, and cost-efficiency
• Keep everything running smoothly in real production environments

This is not a “design slides and disappear” role — it’s real ownership of real data systems.

What You’ll Be Doing

Data Engineering & Pipelines

• Build and operate Spark / PySpark workloads on EMR and Glue
• Design end-to-end pipelines:
  API / DB / file ingestion → transformation → delivery to analytics consumers
• Implement data validation, monitoring, and quality checks
• Optimize pipelines for performance, cost, and scalability

Infrastructure & Operations

• Manage AWS infrastructure using Terraform
• Monitor via CloudWatch
• Debug production failures and implement preventive solutions
• Maintain IAM and security best practices

Collaboration

• Work closely with product and analytics teams
• Define clear data contracts
• Deliver reliable datasets for BI and analytics use cases

Must-Have Experience

• 5+ years of hands-on data engineering in production
  (actual pipelines running in production, not only architecture work)
• Strong Spark / PySpark
• Advanced Python
• Advanced SQL
• AWS data stack: EMR, Glue, S3, Redshift (or similar), IAM, CloudWatch
• Infrastructure as Code with Terraform
• Experience debugging and stabilizing production data systems

Nice to Have

• Kafka or Kinesis (streaming)
• Airflow or similar orchestration tools
• Experience supporting BI tools and analytics teams

What We Care About

• You’ve handled pipeline failures in production — and learned from them
• You prioritize data correctness, not just speed
• You write maintainable, readable code
• You understand AWS cost and scaling trade-offs
• You avoid over-engineering — and ship what delivers value