Financial Data Engineer, Stock & AI Data

This is a fully remote position, open to applicants in Hong Kong, +3 more countries.

📋 Description

• Conduct research, perform technical evaluations, ingest, integrate, cleanse, standardize, compute, store, and manage financial market data.

• Manage securities master data, as well as real-time and historical market data, fundamentals, corporate actions, indices, and product and risk data.

• Oversee source ingestion, raw data retention, and ensure stable delivery to knowledge engineering pipelines for announcements, news, and research reports.

• Design scalable, unified data models and integration frameworks across various markets.

• Address trading calendars, time zones, currencies, security identifiers, listing relationships, lifecycles, and data corrections.

• Develop and enhance batch-stream unified data pipelines centered around Flink.

• Optimize latency, throughput, query performance, stability, and cost for trading products, research analysis, and AI scenarios.

• Establish frameworks for data quality and service-level agreements.

• Create automated capabilities for reconciliation, anomaly detection, monitoring and alerting, raw data replay, backfill, and fault recovery.

• Assess vendors, exchanges, APIs, file feeds, and compliance collection sources.

• Collaborate with product, procurement, legal, and compliance teams regarding data usage and redistribution boundaries.

• Define data semantics, metric definitions, and service contracts with trading product, data platform, AI engineering, and algorithm teams.

• Enhance metadata, data lineage, automated testing, CI/CD, 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 field.

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

• Knowledge of stock markets and investor research and decision-making processes.

• Comprehension of trading mechanisms, market data, fundamentals, financial reports, corporate actions, valuation, and significant market events.

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

• Proficiency 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 programming language: Java, Scala, or Python.

• Familiarity with Kafka, Spark, ClickHouse, Doris, HBase, Elasticsearch, or other distributed storage and analytics technologies.

• Understanding of data modeling, task scheduling, metadata, data lineage, data governance, and service level agreements.

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

• High standards for data quality and the capability to design reproducible reconciliation, anomaly detection, backfill, and degradation strategies.

• Experience with data source selection or production ingestion.

• Ability to articulate build-versus-buy decisions, multi-source verification, vendor dependencies, and trade-offs for alternative solutions.

• Strong business acumen and cross-team collaboration skills.

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

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

• Bonus: Familiarity with the US stock market structure, trading calendars, pre/post-market sessions, corporate actions, and adjustment rules.

• Bonus: Data experience with stock-related derivatives, ETFs, indices, or tokenized products.

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

• Bonus: Experience in data anomaly detection, knowledge graphs, financial entity alignment, or high-quality financial datasets for LLMs and RAG.

🏝️ Benefits

• Excellent opportunities for career development.

• Opportunities for learning and growth.

• Competitive salary and benefits package.

• Flexible working hours.

• Remote-first work arrangement.

• Casual work attire.

• Continuous learning and career advancement.

• Work-from-home options (may vary based on role and team requirements).