Data Engineer

BHFT is a proprietary algorithmic trading firm. Our team manages the full trading cycle, from software development to creating and coding strategies and algorithms.
Our trading operations cover key exchanges. The firm trades across a broad range of asset classes, including equities, equity derivatives, options, commodity futures, rates futures, etc. We employ a diverse and growing array of algorithmic trading strategies, utilizing both High-Frequency Trading (HFT) and Medium-Frequency Trading (MFT) approaches.

Looking ahead, we are expanding into new markets and products. As a dynamic company, we continuously experiment with new markets, tools, and technologies.
We’ve got a team of 200+ professionals, with a strong emphasis on technology—70% are technical specialists in development, infrastructure, testing, and analytics spheres. The remaining part of the team supports our business operations, such as Risks, Compliance, Legal, Operations and more.

With a strong focus on innovation and performance, BHFT is actively expanding its presence in traditional financial markets. We value a results-driven culture, emphasizing collaboration, transparency, and constant improvement, all while offering the flexibility of remote work and a globally distributed team.

Data Engineering team is responsible for designing, building, and maintaining the Data Lake infrastructure, including ingestion pipelines, storage systems, and internal tooling for reliable, scalable access to market data.
 

Key Responsibilities

• Ingestion & Pipelines: Architect batch + stream pipelines (Airflow, Kafka, dbt) for diverse structured and unstructured marked data. Provide reusable SDKs in Python and Go for internal data producers.

• Storage & Modeling: Implement and tune S3, column‑oriented and time‑series data storage for petabyte‑scale analytics; own partitioning, compression, TTL, versioning and cost optimisation.

• Tooling & Libraries: Develop internal libraries for schema management, data contracts, validation and lineage; contribute to shared libraries and services for internal data consumers for research, backtesting and real-time trading purposes.

• Reliability & Observability: Embed monitoring, alerting, SLAs, SLOs and CI/CD; champion automated testing, data quality dashboards and incident runbooks.

• Collaboration: Partner with Data Science, Quant Research, Backend and DevOps to translate requirements into platform capabilities and evangelise best practices.

• 5+ years of experience building and maintaining production-grade data systems, with proven expertise in architecting and launching data lakes from scratch.
• Expert-level Python development skills (Go and C++ nice to have).
• Hands-on experience with modern orchestration tools (Airflow) and streaming platforms (Kafka).
• Advanced SQL skills including complex aggregations, window functions, query optimization, and indexing.
• Experience designing high-throughput APIs (REST/gRPC) and data access libraries.
• Solid fundamentals in Linux, containerization (Docker), and cloud object storage solutions (AWS S3, GCS).
• Strong knowledge of handling diverse data formats including structured and unstructured data, with experience optimizing storage strategies such as partitioning, compression, and cost management.
• Fluency in English for confident communication, documentation, and collaboration within an international team.

What we offer:

• Working in a modern international technology company without bureaucracy, legacy systems, or technical debt.
• Excellent opportunities for professional growth and self-realization.
• We work remotely from anywhere in the world, with a flexible schedule.
• We offer compensation for health insurance, sports activities, and professional training.