Design and build scalable backend systems using Java, with deep expertise in production-grade application development.
Lead the architecture and implementation of distributed systems, ensuring reliability, scalability, and performance across complex multi-service environments handling large-scale data processing.
Develop and optimize concurrent and multi-threaded applications, implementing thread synchronization patterns, locks, barriers, and atomic operations for high-performance, data-intensive systems.
Build and maintain big data pipelines and infrastructure to handle high-volume data ingestion, processing, and storage at scale.
Manage containerized workloads using Kubernetes, defining deployment strategies, scaling policies, and resource management for backend services processing big data workloads.
Mentor junior and mid-level engineers on backend best practices, system design, and performance optimization techniques.
Collaborate with cross-functional teams to define service contracts, APIs, and integration patterns.
Implement observability and monitoring solutions to ensure system health and performance visibility.
Support production deployments, troubleshoot complex issues, and drive continuous improvement initiatives.

Deep proficiency in Java with strong understanding of advanced language features and best practices.
Solid expertise in concurrency patterns, multi-threading, thread synchronization (locks, barriers, mutexes), atomic operations, and volatile semantics.
Proven hands-on experience designing and implementing distributed systems at scale.
Strong SQL expertise with ability to design and optimize database queries and schemas.
Hands-on experience with Apache Spark and Apache Airflow for data processing and pipeline orchestration.
Practical knowledge of Kubernetes for container orchestration and service management.
Experience with system profiling, performance analysis, and optimization techniques.
Familiarity with monitoring and observability tools such as Grafana, Prometheus, or Loki.
Spring Framework or other modern Java backend frameworks is a plus.
Kafka or event-streaming architectures is a plus.
Reactive programming frameworks or reactive systems design is a plus.
Apache Pinot or similar analytical data stores is a plus.
Terraform or Infrastructure as Code is a plus.
GitLab CI, Jenkins (Groovy), or similar CI/CD tools is a plus.
GCP or other cloud platforms is a plus.
AI-assisted development tools (GitHub Copilot, Claude, etc.) is a plus.

English: Advanced (required for effective communication with global teams)

Design and build scalable backend systems using Java, with deep expertise in production-grade application development.
Lead the architecture and implementation of distributed systems, ensuring reliability, scalability, and performance across complex multi-service environments handling large-scale data processing.
Develop and optimize concurrent and multi-threaded applications, implementing thread synchronization patterns, locks, barriers, and atomic operations for high-performance, data-intensive systems.
Build and maintain big data pipelines and infrastructure to handle high-volume data ingestion, processing, and storage at scale.
Manage containerized workloads using Kubernetes, defining deployment strategies, scaling policies, and resource management for backend services processing big data workloads.
Mentor junior and mid-level engineers on backend best practices, system design, and performance optimization techniques.
Collaborate with cross-functional teams to define service contracts, APIs, and integration patterns.
Implement observability and monitoring solutions to ensure system health and performance visibility.
Support production deployments, troubleshoot complex issues, and drive continuous improvement initiatives.

Deep proficiency in Java with strong understanding of advanced language features and best practices.
Solid expertise in concurrency patterns, multi-threading, thread synchronization (locks, barriers, mutexes), atomic operations, and volatile semantics.
Proven hands-on experience designing and implementing distributed systems at scale.
Strong SQL expertise with ability to design and optimize database queries and schemas.
Hands-on experience with Apache Spark and Apache Airflow for data processing and pipeline orchestration.
Practical knowledge of Kubernetes for container orchestration and service management.
Experience with system profiling, performance analysis, and optimization techniques.
Familiarity with monitoring and observability tools such as Grafana, Prometheus, or Loki.
Spring Framework or other modern Java backend frameworks is a plus.
Kafka or event-streaming architectures is a plus.
Reactive programming frameworks or reactive systems design is a plus.
Apache Pinot or similar analytical data stores is a plus.
Terraform or Infrastructure as Code is a plus.
GitLab CI, Jenkins (Groovy), or similar CI/CD tools is a plus.
GCP or other cloud platforms is a plus.
AI-assisted development tools (GitHub Copilot, Claude, etc.) is a plus.

English: Advanced (required for effective communication with global teams)

Lead Backend Data Engineer

Skills