Market Data Engineer – Domain Trading Expertise Required

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BHFT

11 - 50 employees

Today’s financial markets are more challenging than ever before. As a result, the reward of success with each day increasingly relies on defining and developing the ideal strategy that can anticipate its movements and latest trends - at BHFT, we specialize in providing this solution. There’s a ton of automated trading platforms around the globe that use multiple quantitative algorithmic strategies, high-frequency algorithms’ race is endless. Still the approach to strategy development is what makes a difference. Our advanced algorithmic trading fintech solutions allow traders worldwide to take full advantage of our state-of-the-art technology and software developed by our team of traders with over 30 years of experience in global trading. We believe that alpha derives from complexity, a minimal level of it must be reached in every aspect of the trading business, starting from business structure and down to the hardware one uses. When a team achieves this result, it can ultimately handle any market condition. Our vision is that quantitative trading is not only a set of automated trading strategies and top algotrading coding, but so much more that makes the algorithmic trading system perfect! We conduct our own research on market microstructure, dynamics of liquidity flows in fragmented markets, and specifics of exchanges of interest. All research is production-oriented, and its results quickly find their way to production through trading logic improvements. Our industry-standard backtesting infrastructure is a must for any algorithmic trading company. Its purpose is to test our hypotheses against the actual market data; however, our main strength arises from our custom trading algorithmic strategies customary for pure-HFT companies. Our trading platform is a custom-tailored solution where flexibility, speed, and performance all come together in sync to yield the best results.

📋 Description

• Capture & Ingestion: Own the full capture path from wire to lake • Build batch + stream pipelines (Airflow, Spark, dbt) for tick and reference data • Own L2/L3 order-book reconstruction with gap handling • Storage & Modeling: Own the Iceberg-over-S3 lakehouse • Drive storage cost optimisation via compaction, tiering, and snapshot expiry • Tooling & Libraries: Build libraries for schema management, data contracts, validation, and lineage • Reliability & Observability: Embed monitoring, alerting, SLAs/SLOs, and CI/CD across capture and pipeline layers on Kubernetes • Collaboration: Partner with Quant Research, Data Science, Backend, and DevOps to translate requirements into platform capabilities

🎯 Requirements

• 5+ years building production-grade data systems • Proven expertise architecting and launching data lakes / lakehouses from scratch • Hands-on experience with Apache Iceberg (or comparable table formats — Delta / Hudi) • Experience with market data and/or network packet capture • Experience normalizing market data from multiple vendors • Expert-level Python (incl. Polars and/or PySpark); Rust a strong plus • Modern orchestration (Airflow) and distributed processing (Apache Spark) • Advanced SQL: complex aggregations, window functions, query optimization, partition pruning • Solid fundamentals in Linux, containerization (Docker, Kubernetes / EKS), and cloud object storage (AWS S3) • DevOps & observability: CI/CD, infrastructure-as-code (Terraform), GitOps (ArgoCD) • Strong grasp of structured + unstructured / binary data, and storage optimization • English fluency for documentation and collaboration in an international team

🏖️ Benefits

• Compensation for health insurance • Compensation for sports • Compensation for professional development • Fully remote work from anywhere in the world, on a flexible schedule

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