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Binance is seeking a Senior Financial Data Engineer to build and own the core data infrastructure for its stock and related financial market businesses. You will be responsible for the complete pipeline from data source discovery, evaluation, and ingestion through unified modeling, real-time processing, quality governance, and data services delivery.
Key responsibilities include:
• Own the full lifecycle of financial market data—securities master data, real-time and historical market quotes, fundamentals, corporate actions, indices, and product/risk data. For content-type data (announcements, news, research reports), manage source ingestion, raw retention, and stable delivery to knowledge engineering pipelines.
• Design scalable unified data models and ingestion frameworks that handle varying market conventions for trading calendars, time zones, currencies, security identifiers, listing relationships, lifecycle events, and data corrections, enabling rapid onboarding of new markets and sources.
• Build and optimize batch-stream unified data pipelines centered on Flink, continuously improving latency, throughput, query performance, stability, and cost while supporting trading products, research, analysis, and AI use cases.
• Establish data quality and service-level frameworks with ownership of completeness, accuracy, timeliness, consistency, and traceability. Build automated reconciliation, anomaly detection, monitoring, alerting, raw data replay, backfill, and disaster recovery capabilities.
• Evaluate coverage, quality, stability, revision mechanisms, and technical compatibility of various data sources (vendors, exchanges, APIs, file feeds, compliant collection). Collaborate with product, procurement, legal, and compliance teams to define usage boundaries and drive primary/backup source and fallback strategies.
• Partner with trading product, data platform, AI engineering, and algorithm teams to define data semantics, metric definitions, and service contracts, ensuring consistent and reliable use of stock facts across products.
• Drive data engineering efficiency and technical quality improvements including metadata management, 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, with 5+ years of experience in data engineering, big data, or data platforms.
• Familiar with stock markets and investor research/decision-making workflows; understands trading mechanics, market quotes, fundamentals, financial reports, corporate actions, valuation, and major market events. Able to explain the full pipeline of at least one type of financial data from source to end-user product, including 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 distributed storage/analytics technologies such as ClickHouse, Doris, HBase, Elasticsearch, or similar.
• 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; capable of designing reproducible reconciliation, anomaly detection, backfill, and degradation strategies.
• Experience with data source selection or production ingestion; able to articulate trade-offs between buy vs. build, multi-source verification, vendor dependency, and fallback alternatives.
• Strong business understanding and cross-team collaboration skills; able to translate trading, risk, research, or AI problems into clear data models and data contracts.
Preferred qualifications:
• Experience with stock data at brokerages, market data services, financial data providers, wealth management, or fintech platforms.
• Familiarity with US equity market structure, trading calendars, extended hours, corporate actions, and adjustment rules; experience with other stock markets is a plus.
• Experience with stock-related derivatives, ETFs, indices, or tokenized products.
• Track record 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 constructing high-quality financial datasets for large language models and retrieval-augmented generation (RAG).
