Big Data Engineer

Description

Final is a world leader in trading algorithms and trade execution technologies development. Our multi-disciplinary teams have developed a unique and highly successful machine learning algorithmic based HFT platform that delivers excellent results. In a world increasingly dominated by learning machines and artificial intelligence, we at Final are especially proud of our humans. Our elite team of exceptional people are the soul of our company, and it is our top priority to provide them with a professionally fulfilling environment that supports healthy work-life balance. Our employees are encouraged to pursue their passions outside of work and we are proud to offer them a variety of opportunities, multiple resources and an agile work environment which promotes their well-being.

We are looking for a skilled and motivated Big Data Engineer to join our backend data infrastructure team. You will be at the core of Final’s data ecosystem, building and maintaining high-performance data services and pipelines that support both real-time and batch workloads. Your work will directly impact how data is accessed and leveraged across the company — from live production environments to ML training pipelines. You will design and maintain systems that span hybrid infrastructure (on-prem and cloud), and ensure our data platform is fast, reliable, and scalable. We value engineers who are curious, open-minded, and excited to learn new technologies and practices as the landscape evolves.

As a Big Data Engineer, You Will

• Design, implement, and maintain backend services for managing and processing large-scale data.
• Build and operate production-grade data pipelines and infrastructure.
• Develop utilities ,libraries and services to support high-throughput data retrieval and access patterns.
• Ensure observability, stability, and performance of data services in hybrid (cloud/on-prem) environments.
• Monitor and troubleshoot issues in live systems and continuously improve their robustness.
• Work cross-functionally to ensure data is accessible, well-modeled, and easy to consume by other teams.
  
  

• Strong programming experience in at least one of the following: C++, Java, Rust, .NET, or Python.
• Experience working with python data analytics libraries (such as numpy, pandas, polars).
• Experience working on backend services or data-intensive applications.
• Understanding of distributed systems, data pipelines, and production monitoring.
• Experience in hybrid infrastructure environments (on-prem + cloud).
• An open-minded technologist with a willingness to learn and adopt new technologies and best practices.
  
  

• Familiarity with Apache Iceberg or other table/data format technologies (e.g., Delta Lake, Hudi, Parquet, ORC).
• Familiarity with Streaming technologies – Kafka, Flink.
• Experience with orchestration tools like Airflow or Argo.
• Exposure to analytics engines (e.g., Spark, DuckDB, Trino).
• Knowledge of Kubernetes and containerized deployments.
• Experience in MLOps or supporting machine learning pipelines.