Senior Financial Data Engineer

Job not on LinkedIn

🔥 3 minutes ago

🌐 Taiwan, Hong Kong – Remote

🇹🇼 Taiwan – Remote 🇭🇰 Hong Kong – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 11%

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Binance

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2017

₿ Crypto

💳 Fintech

💰 Initial Coin Offering on 2020-12

Crypto • Fintech

Binance is the world's leading cryptocurrency exchange, serving over 235 million registered users across more than 180 countries. The platform offers a wide array of services, including the trading of over 350 cryptocurrencies in Spot, Margin, and Futures markets. Users can also buy and sell crypto via Binance P2P, earn interest through Binance Earn, and engage in NFT trading on the Binance NFT marketplace. Binance provides low transaction fees and diverse payment options, making it a preferred choice for cryptocurrency enthusiasts worldwide.

📋 Description

• Build core data infrastructure for Binance's stock and related financial market businesses • Own the full financial data pipeline from source discovery, evaluation, and ingestion through unified modeling, real-time processing, quality governance, and data services • Research, technically evaluate, ingest, cleanse, standardize, compute, store, and service securities master data, market quotes, fundamentals, corporate actions, indices, and product/risk data • Ingest, retain, and stably deliver announcements, news, and research reports to the knowledge engineering pipeline • Design scalable unified data models and ingestion frameworks for varied market conventions, calendars, time zones, currencies, security identifiers, listing relationships, lifecycle events, and data corrections • Build and optimize batch-stream unified data pipelines centered on Flink • Support trading products, research and analysis, and AI use cases • Establish data quality and service-level frameworks covering completeness, accuracy, timeliness, consistency, and traceability • Build automated reconciliation, anomaly detection, monitoring and alerting, raw data replay, backfill, and disaster recovery capabilities • Evaluate data sources including vendors, exchanges, APIs, file feeds, and compliant collection • Collaborate with product, procurement, legal, and compliance teams on data usage, display, derivation, storage, and redistribution boundaries • Define primary, backup, and fallback data-source strategies • Partner with trading product, data platform, AI engineering, and algorithm teams to define data semantics, metrics, and service contracts • Improve 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 a related field • 5+ years of experience in data engineering, big data, or data platforms • Familiarity with stock markets and investor research and decision-making workflows • Understanding of trading mechanics, market quotes, fundamentals and financial reports, corporate actions, valuation, and major market events • Ability to explain the full pipeline of at least one type of financial data from source to end-user product, including key quality risks • Proficiency in SQL and Flink • Experience in large-scale real-time data processing, performance tuning, stability governance, and production issue troubleshooting • Proficiency in at least one of Java, Scala, or Python • Familiarity with Kafka, Spark, and distributed storage/analytics technologies such as ClickHouse, Doris, HBase, Elasticsearch, or similar • Familiarity with data modeling, task scheduling, metadata, data lineage, data governance, and service levels • Ability to independently resolve cross-system data consistency issues • Ability to design reproducible reconciliation, anomaly detection, backfill, and degradation strategies • Experience with data source selection or production ingestion • Ability to articulate trade-offs between buy vs. build, multi-source verification, vendor dependency, and fallback alternatives • Strong business understanding and cross-team collaboration skills • Ability 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 providers, wealth management, or fintech platforms • Familiarity with US equity market structure, trading calendars, extended hours, corporate actions, and adjustment rules • Experience with stock-related derivatives, ETFs, indices, or tokenized products is advantageous • Experience building low-latency market data pipelines, securities master data platforms, multi-market data models, quantitative research platforms, or large-scale backtesting data systems is advantageous • Experience with data anomaly detection, knowledge graphs, financial entity alignment, or high-quality financial datasets for LLMs and RAG is advantageous

🏖️ Benefits

• Competitive salary and company benefits • Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team) • Opportunities for career growth and continuous learning • Flat organizational structure • Autonomy in an innovative environment • Equal opportunity employer