Financial Data Engineer, AI/LLM

About Binance
Binance is the global leading blockchain ecosystem, operating the world's largest digital asset trading platform by volume, serving over 300 million users across 100+ countries and regions. We are committed to building a more open financial ecosystem and improving global access to financial services.
Binance is continuously building stock and related financial market products for global users. The relevant data will serve user-facing stock products and Binance AI business scenarios. We are seeking professionals with stock market experience to jointly build reliable, scalable data and financial AI capabilities.
 
Role Overview
You will participate in building the core data foundation for Binance's stock and related financial market businesses, responsible for the full pipeline from data source discovery, evaluation, ingestion, and integration to unified modeling, real-time processing, quality governance, and data services. Beyond completing defined integrations, we expect you to leverage industry expertise to continuously identify better data sources and technical solutions, enabling new markets, products, and data to serve trading products and AI quickly and reliably.

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.

Responsibilities

• Conduct research, technical evaluation, ingestion, integration, cleansing, standardization, computation, storage, and servicing of financial market data, covering securities master data, real-time and historical market data, fundamentals, corporate actions, indices, and business-required product and risk data; responsible for source ingestion, raw retention, and stable delivery to knowledge engineering pipelines for content-type data such as announcements, news, and research reports.
• Design scalable unified data models and integration frameworks, handling different markets' trading calendars, time zones, currencies, security identifiers, listing relationships, lifecycles, and data corrections, supporting rapid onboarding of new markets and sources.
• Build batch-stream unified data pipelines centered on Flink, continuously optimizing latency, throughput, query performance, stability, and cost, while supporting consumer trading products, research analysis, and AI scenarios.
• Establish data quality and service level frameworks, taking responsibility for completeness, accuracy, timeliness, consistency, and traceability; build automated reconciliation, anomaly detection, monitoring and alerting, raw data replay, backfill, and fault recovery capabilities.
• 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