Financial Data Engineer, AI/LLM

• Evaluate different sources (vendors, exchanges, APIs, file feeds, compliance collection) for coverage, quality, stability, revision mechanisms, and technical fit; collaborate with product, procurement, legal, and compliance teams to clarify usage, display, derivative, retention, and redistribution boundaries; drive rational primary/backup source strategies and alternatives.

• Define data semantics, metric definitions, and service contracts jointly with trading product, data platform, AI engineering, and algorithm teams, ensuring consistent and reliable usage of the same stock facts across different products.

• Drive data engineering efficiency and technical quality improvements, including metadata, data lineage, automated testing, CI/CD, task orchestration, capacity governance, and AI-assisted development.

Requirements

• Master's degree or above in Computer Science, Software Engineering, Mathematics, Statistics, or related field; 5+ years of experience in data development, big data, or data platforms.

• Familiar with stock markets and the investor research and decision-making workflow; understand trading mechanisms, market data, fundamentals and financial reports, corporate actions, valuation, and major market events; able to explain the complete pipeline of at least one type of financial data from source to user-facing product and key quality risks.

• Proficient in SQL and Flink, with experience in large-scale real-time data processing, performance tuning, stability governance, and production issue troubleshooting.

• Proficient in at least one of Java, Scala, or Python; familiar with Kafka, Spark, and ClickHouse, Doris, HBase, Elasticsearch, or other distributed storage and analytics technologies.

• Familiar with data modeling, task scheduling, metadata, data lineage, data governance, and service levels; able to independently resolve cross-system data consistency issues.

• High standards for data quality; able to design reproducible reconciliation, anomaly detection, backfill, and degradation strategies — not just completing data development tasks.

• Experience with data source selection or production ingestion; able to articulate trade-offs between build vs. buy, multi-source verification, vendor dependency, and alternative solutions.

• Strong business understanding and cross-team collaboration skills; able to translate trading, risk, research, or AI problems into clear data models and data contracts.

Bonus

• Experience with stock data at brokerages, market data services, financial data, wealth management, or fintech platforms.

• Familiarity with US stock market structure, trading calendars, pre/post-market sessions, corporate actions, and adjustment rules; experience with other stock markets also a plus.

• Data experience with stock-related derivatives, ETFs, indices, or tokenized products.

• Experience building low-latency market data pipelines, securities master data platforms, multi-market data models, quantitative research platforms, or large-scale backtesting data systems.

• Experience with data anomaly detection, knowledge graphs, financial entity alignment, or building high-quality financial datasets for LLMs and retrieval-augmented generation (RAG).

Working at Binance

• Be a part of the world’s leading blockchain ecosystem that continues to grow and offers excellent career development opportunities

• Work alongside diverse, world-class talent in an environment where learning and growth opportunities are endless

• Tackle fast-paced, challenging and unique projects

• Work in a truly global organization, with international teams and a flat organizational structure

• Competitive salary and benefits

• Flexible working hours, remote-first, and casual work attire

Learn more about how Binancians embody the organization’s core values , creating a unified culture that enables collaboration, excellence, and growth.

Apply today to be a part of the Web3 revolution!

Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.

• Big Data
• Engineering

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