Senior Financial Data Engineer

This is a fully remote position, open to applicants in Taiwan, +1 more country.

📋 Description

• Develop the essential data infrastructure for Binance's stock and associated financial market operations.

• Take ownership of the complete financial data pipeline, from source identification, assessment, and ingestion to unified modeling, real-time processing, quality governance, and data services.

• Conduct research, technical evaluations, ingestion, cleansing, standardization, computation, storage, and servicing of securities master data, market quotes, fundamentals, corporate actions, indices, and product/risk data.

• Ingest, retain, and effectively deliver announcements, news, and research reports to the knowledge engineering pipeline.

• Design scalable, unified data models and ingestion frameworks to accommodate various market conventions, calendars, time zones, currencies, security identifiers, listing relationships, lifecycle events, and data corrections.

• Construct and optimize unified batch-stream data pipelines centered around Flink.

• Provide support for trading products, research and analysis, and AI initiatives.

• Establish frameworks for data quality and service-level agreements focused on completeness, accuracy, timeliness, consistency, and traceability.

• Develop automated capabilities for reconciliation, anomaly detection, monitoring and alerting, raw data replay, backfilling, and disaster recovery.

• Assess data sources, including vendors, exchanges, APIs, file feeds, and compliant collection methods.

• Collaborate with product, procurement, legal, and compliance teams regarding data usage, display, derivation, storage, and redistribution guidelines.

• Define strategies for primary, backup, and fallback data sources.

• Partner with teams in trading products, data platforms, AI engineering, and algorithms to establish data semantics, metrics, and service agreements.

• Enhance metadata management, data lineage, automated testing, CI/CD processes, task orchestration, capacity governance, and AI-assisted development.

⛳️ Requirements

• A Master's degree or higher in Computer Science, Software Engineering, Mathematics, Statistics, or a related discipline.

• Over 5 years of experience in data engineering, big data, or data platforms.

• Knowledge of stock markets and the workflows involved in investor research and decision-making.

• Understanding of trading mechanics, market quotes, fundamentals, financial reports, corporate actions, valuation, and significant market events.

• Capability to articulate the entire pipeline of at least one type of financial data from source to end-user product, including key quality risks.

• Proficient in SQL and Flink.

• Experience with large-scale real-time data processing, performance tuning, stability governance, and troubleshooting production issues.

• Proficiency in at least one of Java, Scala, or Python.

• Familiarity with technologies such as Kafka, Spark, and distributed storage/analytics solutions like ClickHouse, Doris, HBase, Elasticsearch, or similar.

• Knowledge of data modeling, task scheduling, metadata management, data lineage, data governance, and service levels.

• Ability to independently address cross-system data consistency challenges.

• Skill in designing reproducible reconciliation, anomaly detection, backfill, and degradation strategies.

• Experience with selecting data sources or managing production ingestion.

• Capacity to discuss trade-offs between buy versus build, multi-source verification, vendor dependency, and fallback options.

• Strong understanding of business operations and effective cross-team collaboration skills.

• Ability to translate trading, risk, research, or AI challenges into clear data models and contracts.

• Experience with stock data at brokerages, market data services, financial data providers, wealth management, or fintech platforms.

• Familiarity with the US equity market structure, trading calendars, extended hours, corporate actions, and adjustment rules.

• Experience with stock-related derivatives, ETFs, indices, or tokenized products is a plus.

• Advantageous experience in building low-latency market data pipelines, securities master data platforms, multi-market data models, quantitative research platforms, or large-scale backtesting data systems.

• Beneficial experience with data anomaly detection, knowledge graphs, financial entity alignment, or high-quality financial datasets for LLMs and RAG.

🏝️ Benefits

• Competitive salary and company benefits.

• Work-from-home options (this arrangement may vary based on the specific requirements of the business team).

• Opportunities for professional growth and ongoing learning.

• A flat organizational structure.

• Autonomy within an innovative environment.

• Equal opportunity employer.