Software Engineer

About the Client: Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources. About the Role: We are looking for a Middle-level Software Engineer to work on the core query engine of a large-scale distributed data platform. You will develop features across query planning, optimization, and execution, contribute to performance-critical components, and help evolve the platform's ability to process massive analytical workloads across heterogeneous data sources. This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services. Responsibilities: Develop and maintain features across the query engine — planning, optimization, execution, and data access layers. Write high-quality, performance-conscious code in Java and/or C++. Work with SQL semantics, query plans, and execution operators over large-scale distributed data. Integrate with columnar formats, open table formats, and connectivity drivers used across the platform. Contribute to CI/CD pipelines and automated testing — participate in build, test, and release workflows in Jenkins. Deploy and validate work in Kubernetes (GKE / EKS / AKS) across GCP, AWS, and Azure using Docker. Debug complex issues spanning query planning, distributed execution, memory management, and I/O. Collaborate closely with US-based engineering teams on design reviews, code reviews, and technical decisions. Required Qualifications: Education: B. S. or M. S. in Computer Science, Computer Engineering, or a related technical field. Programming: Strong proficiency in Java or C++, with solid OOP and software design fundamentals. Candidates comfortable with both are especially valued. SQL & Data: Strong SQL skills and solid understanding of relational and analytical data systems, including query execution concepts. Data processing systems experience: Hands-on experience with data processing systems — query engines, distributed databases, ETL/ELT frameworks, or analytical platforms. Engineering experience: 3+ years in backend/systems software engineering. CI/CD & DevOps basics: Practical experience with Jenkins pipelines and modern development workflows. Containers: Working knowledge of Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency). Cloud: Comfortable operating in at least one major cloud (GCP, AWS, or Azure). Version Control: Confident with Git / GitHub workflows. English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team. Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team. Desired Skills: Experience with Apache Arrow (columnar in-memory format). Familiarity with Apache Calcite (SQL parsing, planning, optimization framework). Exposure to Gandiva or LLVM-based expression compilation. Experience with open table formats — Apache Iceberg, Delta Lake, or Hudi. Familiarity with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar). Understanding of query planning, optimization, and execution internals. Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight. Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure. IaC tools such as Terraform. Experience with performance profiling and optimization of latency-sensitive systems.