Market Data Engineer

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BHFT

WebsiteLinkedInAll Job Openings

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: decode and normalize raw exchange feeds (pcap, multicast UDP / ITCH / FIX) and vendor sources (OneTick, Refinitiv, Bloomberg, ICE) into a unified canonical model with nanosecond timestamps. Build batch + stream pipelines (Airflow, Spark, dbt) for tick and reference data. Own L2/L3 order-book reconstruction with gap handling. Provide Python and Rust producer SDKs for internal feed handlers. • Storage & Modeling — Apache Iceberg. Own the Iceberg-over-S3 lakehouse: design partitioning, sort orders, and row-group layout for fast scans; manage schema evolution, snapshots, time travel, compaction, and TTL. Maintain reference data as slowly-changing tables with point-in-time correctness for backtests. Drive storage cost optimisation via compaction, tiering, and snapshot expiry. • Tooling & Libraries. Build libraries for schema management, data contracts, validation, and lineage on top of the Iceberg catalog. Develop shared access services (Spark + Polars) so Research, backtesting, and trading share one normalized data layer, including gap detection and pcap-vs-lake reconciliation. • Reliability & Observability. Embed monitoring, alerting, SLAs/SLOs, and CI/CD across capture and pipeline layers on Kubernetes (EKS). Own data-quality dashboards and incident runbooks for the capture fleet. • Collaboration. Partner with Quant Research, Data Science, Backend, and DevOps to translate requirements into platform capabilities and champion market-data engineering best practices.

🎯 Requirements

• 5+ years building production-grade data systems, with proven expertise architecting and launching data lakes / lakehouses from scratch. • Hands-on experience with Apache Iceberg (or comparable table formats — Delta / Hudi): partitioning, schema evolution, snapshots, compaction, and catalog operations; familiarity with Apache Arrow for zero-copy, columnar in-memory interchange. • Experience with market data and/or network packet capture — decoding pcap, exchange feed protocols (ITCH, FIX/FAST, multicast UDP), order-book reconstruction, and time-series at scale (strong plus; willingness to learn required). • Experience normalizing market data from multiple vendors — e.g. OneTick, Refinitiv/Reuters, Bloomberg, ICE — into a unified schema and symbology (strong plus). • Expert-level Python (incl. Polars and/or PySpark); Rust a strong plus (relevant for high-performance capture/decoding). • 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), and metrics/dashboards/alerting (Grafana, Prometheus). • Strong grasp of structured + unstructured / binary data, and storage optimization — partitioning, compression, cost management. • English fluency for documentation and collaboration in an international team.

🏖️ Benefits

• Compensation for health insurance, sports, professional development, and more.