Data Engineer

The company operates in the financial data domain, building a production-grade analytics and data ingestion platform that processes large-scale financial datasets.

• Design, develop and maintain Apache Spark batch pipelines and Kafka streaming solutions;
• Build serverless microservices using AWS Lambda and Azure Functions;
• Develop data ingestion connectors for databases, file systems, message queues and financial data feeds;
• Write production-grade code in Java, Scala and Python;
• Manage infrastructure using Terraform across multiple environments;
• Containerize and deploy applications using Docker and Kubernetes (EKS/AKS);
• Implement and maintain CI/CD pipelines in GitLab CI;
• Write and maintain unit and integration tests with high code coverage;
• Monitor platform health and troubleshoot production issues in distributed systems.

• 5+ Years of experience in data engineering or backend platform development;
• Proficiency in Java and/or Scala as primary languages with Python as secondary;
• Expertise in Apache Spark including Spark SQL and Spark Streaming;
• Skills in Apache Kafka and event-driven architectures;
• Competency in SQL and data modeling;
• Familiarity with AWS cloud services and serverless computing;
• Nice to have: Knowledge of Terraform for Infrastructure as Code, Skills in Docker and Kubernetes (EKS/AKS), Familiarity with CI/CD pipelines such as GitLab CI, Jenkins or equivalent, Background in cloud data warehouses such as Amazon Redshift, Understanding of Microsoft Azure services and Azure-native tooling, Knowledge of Iceberg, Hudi or Delta Lake.

• No conditions specified