Senior Financial Data Engineer

Own the research, technical evaluation, ingestion, cleansing, standardization, computation, storage, and servicing of financial market data, covering securities master data, real-time and historical market quotes, fundamentals, corporate actions, indices, and product/risk data as required by the business. For content-type data such as announcements, news, and research reports, own source ingestion, raw retention, and stable delivery to the knowledge engineering pipeline.

Design scalable unified data models and ingestion frameworks that handle varying market conventions for trading calendars, time zones, currencies, security identifiers, listing relationships, lifecycle events, and data corrections, enabling rapid onboarding of new markets and sources.

Build batch-stream unified data pipelines centered on Flink, continuously optimizing latency, throughput, query performance, stability, and cost, while supporting consumer-facing trading products, research and analysis, and AI use cases.

Establish data quality and service-level frameworks, taking ownership of completeness, accuracy, timeliness, consistency, and traceability. Build capabilities for automated reconciliation, anomaly detection, monitoring and alerting, raw data replay, backfill, and disaster recovery.

Evaluate the coverage, quality, stability, revision mechanisms, and technical compatibility of various data sources—including vendors, exchanges, APIs, file feeds, and compliant collection. Collaborate with product, procurement, legal, and compliance teams to define boundaries for usage, display, derivation, storage, and redistribution, and drive reasonable primary/backup source and fallback strategies.

Partner with trading product, data platform, AI engineering, and algorithm teams to jointly define data semantics, metric definitions, and service contracts, ensuring that the same stock facts can be used consistently and reliably across different products.

Drive data engineering efficiency and technical quality improvements, including metadata management, data lineage, automated testing, CI/CD, task orchestration, capacity governance, and AI-assisted development.

Master's degree or above in Computer Science, Software Engineering, Mathematics, Statistics, or a related field, with 5+ years of experience in data engineering, big data, or data platforms.

Familiar with stock markets and the investor research and decision-making workflow; understands trading mechanics, market quotes, fundamentals and financial reports, corporate actions, valuation, and major market events. Able to explain the full pipeline of at least one type of financial data from source to end-user product, including key quality risks.

Proficient in SQL and Flink, with experience in large-scale real-time data processing, performance tuning, stability governance, and production issue troubleshooting.

Proficient in at least one of Java, Scala, or Python; familiar with Kafka, Spark, and distributed storage/analytics technologies such as ClickHouse, Doris, HBase, Elasticsearch, or similar.

Familiar with data modeling, task scheduling, metadata, data lineage, data governance, and service levels; able to independently resolve cross-system data consistency issues.

Holds high standards for data quality, capable of designing reproducible reconciliation, anomaly detection, backfill, and degradation strategies—not just completing data development tasks.

Has experience with data source selection or production ingestion, able to articulate trade-offs between buy vs. build, multi-source verification, vendor dependency, and fallback alternatives.

Strong business understanding and cross-team collaboration skills; able 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.

Familiar with US equity market structure, trading calendars, extended hours, corporate actions, and adjustment rules; experience with other stock markets is also a plus.

Experience with stock-related derivatives, ETFs, indices, or tokenized products.

Has built 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 constructing high-quality financial datasets for large language models and retrieval-augmented generation (RAG).