Financial Data Engineer, Stock & AI Data

Binance; Australia; À distance; AUD 130 000 - 180 000; Plein temps

Avantages offerts par ce poste

Competitive salary

Remote-first environment

Résumé du poste

Binance is building a core data foundation for its stock and financial market businesses. You will own the full pipeline from discovery and ingestion to unified modeling and real-time processing, ensuring quality and governance across source systems.

You will craft scalable data models and frameworks for multiple markets, currencies, and products. You will collaborate with product, data platform, and AI teams to enable new markets and AI-enabled trading capabilities while optimizing latency and

Qualifications

• Master's degree or above with 5+ years in data development, big data, or data platforms.
• Proficient in SQL and Flink with large-scale real-time data processing; capable of performance tuning and governance.
• Proficient in Java or Python; familiar with distributed storage and analytics tech (Kafka, Spark, ClickHouse, Doris, HBase, Elasticsearch).
• Understand data modeling, metadata, lineage, governance and service levels; able to resolve cross-system data consistency issues.
• High standards for data quality; design reproducible reconciliation, anomaly detection, backfill, and degradation strategies.

Responsabilités

• Conduct end-to-end data pipeline work: discovery, ingestion, integration, cleansing, and delivery for financial market data.
• Design scalable unified data models and integration frameworks for multiple markets and currencies.
• Build batch-stream pipelines centered on Flink to optimize latency, throughput, and cost for trading products and AI needs.
• Establish data quality and service level frameworks with monitoring, alerting, and data replay capabilities.
• Evaluate data sources (vendors, exchanges, APIs, feeds) for coverage, quality, and fit; collaborate on source strategies.
• Define data semantics, metrics, and service contracts with cross-functional teams to ensure consistent stock facts.

Connaissances

SQL

Flink

Java

Python

Kafka

Spark

ClickHouse

Elasticsearch

Data modeling

Data governance

Formation

Master's degree or above in CS/SE/Math/Statistics

Outils

Kafka

Spark

ClickHouse

Doris

HBase

Elasticsearch

Description du poste

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, and more. Our mission is to increase the freedom of money and financial access for people around the world.

About Binance

Binance is the global leading blockchain ecosystem, operating the world's largest digital asset trading platform by volume, serving over 300 million users across 100+ countries and regions. We are committed to building a more open financial ecosystem and improving global access to financial services. Binance is continuously building stock and related financial market products for global users. The relevant data will serve user-facing stock products and Binance AI business scenarios. We are seeking professionals with stock market experience to jointly build reliable, scalable data and financial AI capabilities.

Role Overview

You will participate in building the core data foundation for Binance's stock and related financial market businesses, responsible for the full pipeline from data source discovery, evaluation, ingestion, and integration to unified modeling, real-time processing, quality governance, and data services. Beyond completing defined integrations, we expect you to leverage industry expertise to continuously identify better data sources and technical solutions, enabling new markets, products, and data to serve trading products and AI quickly and reliably.

Working at Binance

• Be a part of the world’s leading blockchain ecosystem that continues to grow and offers excellent career development opportunities
• Work alongside diverse, world-class talent in an environment where learning and growth opportunities are endless
• Tackle fast-paced, challenging and unique projects
• Work in a truly global organization, with international teams and a flat organizational structure
• Competitive salary and benefits
• Flexible working hours, remote-first, and casual work attire

Learn more about how Binancians embody the organization’score values, creating a unified culture that enables collaboration, excellence, and growth.

Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.

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; responsible for source ingestion, raw retention, and stable delivery to knowledge engineering pipelines for content-type data such as announcements, news, and research reports.
• Design scalable unified data models and integration frameworks, handling different markets' trading calendars, time zones, currencies, security identifiers, listing relationships, lifecycles, and data corrections, supporting 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 trading products, research analysis, and AI scenarios.
• Establish data quality and service level frameworks, taking responsibility for completeness, accuracy, timeliness, consistency, and traceability; build automated reconciliation, anomaly detection, monitoring and alerting, raw data replay, backfill, and fault recovery capabilities.
• Evaluate different sources (vendors, exchanges, APIs, file feeds, compliance collection) for coverage, quality, stability, revision mechanisms, and technical fit; collaborate with product, procurement, legal, and compliance teams to clarify usage, display, derivative, retention, and redistribution boundaries; drive rational primary/backup source strategies and alternatives.
• Define data semantics, metric definitions, and service contracts jointly with trading product, data platform, AI engineering, and algorithm teams, ensuring consistent and reliable usage of the same stock facts across different products.
• Drive data engineering efficiency and technical quality improvements, including metadata, data lineage, automated testing, CI/CD, task orchestration, capacity governance, and AI-assisted development.

Requirements

• Master's degree or above in Computer Science, Software Engineering, Mathematics, Statistics, or related field; 5+ years of experience in data development, big data, or data platforms.
• Familiar with stock markets and the investor research and decision‑making workflow; understand trading mechanisms, market data, fundamentals and financial reports, corporate actions, valuation, and major market events; able to explain the complete pipeline of at least one type of financial data from source to user‑facing product and 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 ClickHouse, Doris, HBase, Elasticsearch, or other distributed storage and analytics technologies.
• Familiar with data modeling, task scheduling, metadata, data lineage, data governance, and service levels; able to independently resolve cross‑system data consistency issues.
• High standards for data quality; able to design reproducible reconciliation, anomaly detection, backfill, and degradation strategies — not just completing data develop