Financial Data Engineer, AI/LLM

Binance
Taipei, Hong Kong
Workplace: RemoteFull timeFunction: Data Science & Machine LearningEducation: mastersSkills: ["Cross-team collaboration","Business understanding","Data quality focus","Technical evaluation"]

Build Binance’s core financial data foundation for stock and related market businesses, owning the end-to-end pipeline from data source discovery and ingestion through unified modeling, real-time processing, and data services. Design scalable data models across markets and time zones, implement batch-stream pipelines with Flink, and establish quality governance with reconciliation, anomaly detection, monitoring, and fault recovery. Partner across trading, risk, research, and AI teams to deliver reliable data for products and LLM/RAG scenarios.

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FursaFursa
Binance
Binance
2 years ago

Financial Data Engineer, AI/LLM

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Source: Company careers pageValidated by: Fursa AI
Last checked: 5 hours agoStatus: Live

Job Summary

Build Binance’s core financial data foundation for stock and related market businesses, owning the end-to-end pipeline from data source discovery and ingestion through unified modeling, real-time processing, and data services. Design scalable data models across markets and time zones, implement batch-stream pipelines with Flink, and establish quality governance with reconciliation, anomaly detection, monitoring, and fault recovery. Partner across trading, risk, research, and AI teams to deliver reliable data for products and LLM/RAG scenarios.
Location: Taipei, Hong Kong
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Own the end-to-end financial data pipeline from data source discovery and evaluation through ingestion, integration, unified modeling, and real-time data processing.
  • •Design scalable unified data models and integration frameworks across markets, trading calendars, time zones, currencies, and security identifiers, supporting rapid onboarding of new markets and sources.
  • •Build batch-stream unified pipelines centered on Flink, optimizing latency, throughput, query performance, stability, and cost for trading products, research analysis, and AI scenarios.
  • •Establish data quality and service level frameworks with automated reconciliation, anomaly detection, monitoring/alerting, raw data replay, backfill, and fault recovery.
  • •Define data semantics, metric definitions, and service contracts with trading product, data platform, AI engineering, and algorithm teams to ensure consistent usage of the same stock facts.

Key Requirements

  • •Master's degree or above in Computer Science, Software Engineering, Mathematics, Statistics, or a related field.
  • •5+ years of experience in data development, big data, or data platforms.
  • •Understand stock markets and the investor research/decision workflow, including trading mechanisms, market data, fundamentals, financial reports, corporate actions, valuation, and major market events.
  • •Proficient in SQL and Flink, with experience in large-scale real-time data processing and production issue troubleshooting.
  • •Proficient in Java, Scala, or Python, and familiar with distributed systems such as Kafka, Spark, and ClickHouse, Doris, HBase, or Elasticsearch.
Experience:Big dataData platformsStock marketsFintech
Education:Master's
Skills:Cross-team collaborationBusiness understandingData quality focusTechnical evaluation
Tech Stack:SQLFlinkJavaScalaPythonKafkaSparkClickHouseDorisHBaseElasticsearchCI/CDCI CDTask orchestrationMonitoringAnomaly detectionKnowledge graphsRAG

Company Brief

Binance
Operates one of the world’s largest cryptocurrency exchanges, offering spot and derivatives trading, a native token (BNB), wallet services, staking, and a broad suite of crypto financial products and infrastructure.
Industry: Trading Platforms
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Established Company
Valuation: Unicorn (USD 1B+)
Funding: Bootstrapped
Headquarters: George Town, Cayman Islands
Founded: 2017
WebsiteLinkedIn