Senior Data Engineer

About the project

Our client is a global company with a platform managing over 700,000 hotel rooms across 124 countries, helping clients generate billions in revenue.

Why join

— Stable long-term cooperation: already 5 engineers are working on the project
— Technical freedom: ability to propose architecture decisions and improvements without heavy bureaucracy
— End-to-end ownership: work with the full data pipeline from raw data to production-ready solutions
— Direct business impact: your work helps increase hotel revenues
— Collaboration with major global hotel brands
— Fast and measurable results with clear business value

Requirements

Must have

— 5+ years of experience in Data Engineering
— 3+ years of commercial experience with AWS (including at least 1 year on the latest project)
— 2+ years of experience with Spark/PySpark (including at least 1 year on the latest project)
— Strong experience with:

• Spark / PySpark
• AWS S3
• Kafka or Kinesis
• DynamoDB
• PostgreSQL or MySQL
• AWS ECS Fargate

Nice to have

• AWS Glue
• EMR
• AWS Batch
• Flink
• Apache Beam
• Cassandra / ScyllaDB
• AWS Step Functions

Responsibilities

• Implement scaling of the case detection process (scanner)
• Split monolithic API into multiple services
• Emulate Pub/Sub and Cloud Run locally
• Extract and process data using PySpark + S3
• Work with Kafka/Kinesis pipelines (as a plus)
• Write and optimize SQL queries in PostgreSQL/Redshift
• Use DynamoDB as a data source and sink
• Run and debug ETL jobs on AWS Glue or EMR
• Monitor jobs via AWS Console and Spark UI