Financial Data Engineer, Stock & AI Data
New
B
BinanceFinancial data
Location: Australia; Hong Kong; Singapore; TaiwanFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in data development, big data, or data platforms.
- Required Skills
- PythonSQLJavaKafkaClickhouseSparkScalaData modeling
Requirements
- Hold a master’s degree or above in Computer Science, Software Engineering, Mathematics, Statistics, or a related field.
- Have 5+ years of experience in data development, big data, or data platforms.
- Understand stock markets and investor research and decision-making workflows, including trading mechanisms, market data, fundamentals, financial reports, corporate actions, valuation, and major market events.
- Be able to explain the end-to-end pipeline of at least one financial data type, from source to user-facing product, and identify key quality risks.
- Be proficient in SQL and Flink, with experience in large-scale real-time data processing, performance tuning, stability governance, and production troubleshooting.
- Be proficient in at least one of Java, Scala, or Python.
- Be familiar with Kafka, Spark, and distributed storage and analytics technologies such as ClickHouse, Doris, HBase, or Elasticsearch.
- Be familiar with data modeling, task scheduling, metadata, data lineage, data governance, and service levels, and be able to resolve cross-system data consistency issues independently.
- Be able to design reproducible reconciliation, anomaly detection, backfill, and degradation strategies.
- Have experience selecting data sources or ingesting data into production, and be able to explain build-versus-buy, multi-source verification, vendor dependency, and alternative-solution trade-offs.
- Understand stock markets and financial data sources, or have experience with stock data, financial data, wealth management, or fintech platforms.
Responsibilities
- Research, evaluate, ingest, integrate, cleanse, standardize, compute, store, and serve financial-market data, including securities master data, market data, fundamentals, corporate actions, and indices.
- Ingest and retain raw content-type data such as announcements, news, and research reports, and deliver it to knowledge engineering pipelines.
- Design unified data models and integration frameworks that account for market calendars, time zones, currencies, security identifiers, listing relationships, lifecycles, and data corrections.
- Build batch and stream data pipelines centered on Flink, optimizing latency, throughput, query performance, stability, and cost.
- Establish data quality and service-level frameworks, including automated reconciliation, anomaly detection, monitoring, alerting, replay, backfill, and fault recovery.
- Evaluate data vendors, exchanges, APIs, file feeds, and compliance collection sources; develop primary, backup, and alternative source strategies.
- Collaborate with product, procurement, legal, and compliance teams on data usage, display, derivative, retention, and redistribution boundaries.
- Define data semantics, metric definitions, and service contracts with trading product, data platform, AI engineering, and algorithm teams.
- Improve metadata, data lineage, automated testing, CI/CD, task orchestration, capacity governance, and AI-assisted development.
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