Financial Data Engineer, AI/LLM at Binance

Role Overview

You will be a key participant in building the core data foundation for Binance's stock and related financial market businesses. This role is responsible for the entire pipeline, from initial data source discovery and evaluation, through ingestion and integration, to unified modeling, real-time processing, quality governance, and data service delivery. Beyond fulfilling defined integration tasks, we expect you to use your industry expertise to continually identify superior data sources and technical solutions, thereby enabling the rapid and reliable delivery of new markets, products, and data to serve trading products and AI.

Working at Binance means being part of the world's leading blockchain ecosystem that continues to expand, offering excellent career development opportunities. You will collaborate with diverse, world-class talent in an environment defined by continuous learning and growth. You will tackle fast paced, challenging, and unique projects within a truly global organization that maintains a flat structure. We offer a competitive salary and benefits along with flexible working hours, a remote-first philosophy, and a casual work attire policy.

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. This includes managing source ingestion, raw data retention, and stable delivery to knowledge engineering pipelines for content types such as announcements, news, and research reports.
• Design scalable unified data models and integration frameworks, managing diverse requirements including different markets' trading calendars, time zones, currencies, security identifiers, listing relationships, lifecycles, and data corrections, supporting the quick onboarding of new markets and sources.
• Build batch-stream unified data pipelines centered on Flink, with a continuous focus on optimizing latency, throughput, query performance, stability, and cost, while supporting consumer trading products, research analysis, and AI scenarios.
• Establish comprehensive data quality and service level frameworks, taking ownership of completeness, accuracy, timeliness, consistency, and traceability. This involves building automated reconciliation, anomaly detection, monitoring, alerting, raw data replay, backfill, and fault recovery capabilities.
• Evaluate various sources, including vendors, exchanges, APIs, file feeds, and compliance collections, assessing coverage, quality, stability, revision mechanisms, and technical fit. You will collaborate with product, procurement, legal, and compliance teams to clarify usage, display, derivative, retention, and redistribution boundaries, and drive rational primary/backup source strategies and alternatives.
• Define data semantics, metric definitions, and service contracts in joint consultation with trading product, data platform, AI engineering, and algorithm teams, ensuring consistent and reliable usage of the same stock facts across all products.
• Drive data engineering efficiency and technical quality improvements by implementing metadata management, data lineage tracking, automated testing, CI/CD pipelines, 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, coupled with 5 or more years of experience in data development, big data, or data platforms.
• Familiarity with stock markets and the investor research and decision-making workflow; you must understand trading mechanisms, market data, fundamentals, financial reports, corporate actions, valuation, and major market events. You should be able to explain the complete pipeline of at least one type of financial data, from source to user facing product, and detail the key quality risks.
• Proficiency in SQL and Flink, with hands on experience in large scale real-time data processing, performance tuning, stability governance, and resolving production issues.
• Proficiency in at least one language from Java, Scala, or Python. Familiarity with technologies such as Kafka, Spark, ClickHouse, Doris, HBase, or Elasticsearch, or other distributed storage and analytics technologies is required.
• Familiarity with data modeling, task scheduling, metadata, data lineage, data governance, and service levels. You must be able to independently resolve cross system data consistency issues.
• Maintain high standards for data quality; capable of designing reproducible reconciliation, anomaly detection, backfill, and degradation strategies, going beyond simple data development tasks.
• Experience with data source selection or production ingestion; able to clearly articulate trade offs between build versus buy, multi source verification, vendor dependency, and alternative solutions.
• Strong business acumen and cross team collaboration skills, enabling the translation of trading, risk, research, or AI problems into clear data models and data contracts.

Bonus Qualifications

• Experience dealing 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 is a plus.
• Data experience involving 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).