Data Architect
BHFT
Employment Type
Full Time
Location
Dubai
Requirements
Required Skills
Job Description
Responsibilities
- Architect and standardize streaming and batch pipelines, ensuring reliability, fault tolerance, and scalability.
- Own the architecture and evolution of the Data Lakehouse, ensuring consistency between research (offline) and live trading (online) environments.
- Define and own the end-to-end architecture for the market data platform, covering ingestion, processing, storage, distribution, and access.
- Design scalable, low-latency systems for real-time and historical market data across multiple global exchanges and asset classes.
- Establish data models, schemas, storage strategies, and technology choices optimized for high-volume time-series financial data.
- Provide architectural leadership across the full development lifecycle, including design reviews, standards, and technical integration of team deliverables.
- Collaborate closely with quantitative researchers, trading, and engineering teams to enable fast experimentation and reliable production deployment.
- Drive performance optimization, capacity planning, and long-term scalability of the data ecosystem.
- Document architectural decisions and maintain technical roadmaps to ensure high engineering standards and platform consistency.
Requirements
- Deep understanding of market data (tick, order book, trades, reference, derivatives) and its use in HFT/MFT and systematic trading environments.
- Strong financial markets background to make informed architectural decisions on storage engines, schemas, and latency trade-offs.
- Proven expertise in distributed data systems (streaming, batch, and hybrid architectures) and scalable storage formats.
- Experience designing and operating low-latency, production-critical data pipelines with strict SLAs.
- Advanced data modeling skills, particularly for large-scale time-series financial datasets.
- End-to-end architectural vision across ingestion, processing, storage, serving, and feature computation layers.
- Experience architecting and integrating Feature Stores with consistency between offline research and online trading systems.
- Strong ownership of data quality, governance, monitoring, lineage, and observability frameworks.
- Ability to evaluate trade-offs (latency vs cost, flexibility vs performance, consistency vs availability) and make structured architectural decisions.
- Technical leadership capability: setting standards, reviewing designs, mentoring engineers, and ensuring cohesive integration across teams.
Preferred Qualifications
- Experience with cloud platforms (AWS, GCP, Azure) and distributed data processing frameworks (Spark, Flink, Kafka, etc.).
- Familiarity with financial data standards and protocols (FIX, XML, JSON, etc.).
- Strong knowledge of data governance, data lineage, and data quality monitoring tools.
- Experience with machine learning and data science workflows.
- Familiarity with DevOps practices and CI/CD pipelines.
Benefits
- 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, professional training, and more.
About the Company
BHFT is a proprietary algorithmic trading firm managing the full trading lifecycle — from software development to designing and deploying trading strategies. We operate across major exchanges and a wide range of asset classes, including equities, derivatives, options, commodities, and rates, using both high-frequency (HFT) and medium-frequency (MFT) approaches.
We are a 230-person company with a strong technology focus, where 70% of the team are engineers and technical specialists. As we expand into new markets and products, we foster a results-driven culture built on innovation, collaboration, and continuous improvement, supported by a globally distributed, remote-friendly team.
How to Apply
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