Binance - Financial Data Engineer, Stock & AI Data
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. • 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).
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.
Benefits
• Shape the future of finance • Collaborate with world-class talent in a user-centric global organization with a flat structure • Tackle challenging projects in a fast-paced and innovative enviornment with autonomy • Thrive in a results-driven workplace with opportunities for career growth and continuous learning • Competitive salary and company benefits • Remote-first, work-from-home arrangement (may vary depending on the nature of the role and team)
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