Data Enginner

Data pipelines and streaming

• Design, build and maintain real-time and batch data pipelines for market data ingestion
  
• Build streaming pipelines using Apache Kafka — topics, partitioning, consumer groups, schema management
  
• Write distributed processing jobs in PySpark for large-scale transformation and aggregation
  
• Ensure pipelines are fault-tolerant, idempotent and recover cleanly from failures
  

Data lake and storage

• Design and maintain the data lake, including partitioning strategy, file formats and retention
  
• Model data for both analytical querying and low-latency platform reads
  
• Manage schema evolution without breaking downstream consumers
  

Data quality and reliability

• Build validation and reconciliation checks so bad or missing market data is caught before it reaches subscribers
  
• Monitor pipeline health, set up alerting, and own incident resolution and root-cause analysis
  
• Maintain lineage and documentation for critical datasets
  

DevOps and deployment

• Own deployment of data services using Docker and Kubernetes
  
• Build and maintain CI/CD pipelines for data jobs
  
• Manage cloud infrastructure (AWS) for data workloads, with an eye on cost
  
• Work with the DevOps team on infrastructure-as-code and observability
  
  

Requirements

Requirements

• 3+ years in data engineering or a closely related role
  
• Apache Kafka — production experience with streaming pipelines
  
• PySpark — batch and streaming, with an understanding of how to tune a job
  
• Data lake architecture — partitioning, formats such as Parquet, and query performance
  
• DevOps practices — Docker, Kubernetes, CI/CD
  
• Strong Python and SQL
  
• Cloud platform experience, preferably AWS (S3, EMR, Glue or equivalent)
  
• Bachelor's degree in Computer Science, Information Technology or related, or equivalent practical experience