About the Opportunity

You will join our Data & Analytics team, which owns two core data domains end to end:

• Public market data — trades, quotes, funding, and full order-book data continuously captured from global crypto exchanges at petabyte scale.

• Proprietary trading data — orders, fills, positions, balances, and settlement activity streamed from internal desks and systems.

We are one team rather than separate engineering and analytics functions. The same people build pipelines, model data, validate its correctness, and answer the business questions it supports. You will see data problems through end to end: from the exchange connection or internal source through to the analysis produced from it.

This is an exciting time to join. We are rebuilding the platform on ClickHouse, Apache Iceberg, and Kafka while continuing to support live trading. The work combines substantial platform design with hands-on ownership of business-critical data.

What You’ll Be Doing

• Build and operate pipelines that bring market and trading data into our platform, including ingestion, parsing, normalization, validation, and storage.

• Build and extend Python- and SQL-based datasets and data models used by trading desks, quants, research, and analytics teams.

• Own data correctness through source reconciliation, data-quality and completeness checks, gap and duplicate detection, backfills, and replays.

• Investigate discrepancies end to end. Trace unexpected numbers through the pipeline to the source and fix root causes rather than symptoms.

• Onboard new exchanges, venues, products, and internal data sources, addressing real-world external-feed behaviour including authentication, rate limits, disconnects, missing history, and schema changes.

• Support data users through query support, data access, APIs, and client libraries.

• Operate what you build, including monitoring, alerting, and incident response for owned pipelines and datasets.

• Contribute to the migration toward ClickHouse, Iceberg, Kafka, and Kubernetes by running new and legacy paths in parallel, reconciling results, and cutting over safely.

• Use AI coding assistants and LLM tooling to improve development, debugging, and data investigation, while applying sound judgement and independently validating outputs.

• Depending on team needs and your strengths, develop deeper expertise in either real-time platform and pipeline engineering or proprietary trading-data datasets and analysis. Both are core to the team.

What We Look For In You

• Bachelor’s degree or above in Computer Science, Engineering, or a related quantitative field.

• 3–5 years of hands-on experience in data engineering, software engineering, or analytics engineering.

• Strong production-quality programming skills in Java, Python, or both.

• Sound data-modelling judgement. You can design schemas that scale with evolving data and use cases, including keys, granularity, partitioning, and schema evolution.

• Ability to reason about query behaviour and performance over large datasets.

• Demonstrated production ownership. You have designed, delivered, and operated a pipeline, service, or dataset that other teams depend on, including its reliability, monitoring, and failure modes.

• Rigour in data correctness. You validate outputs before they reach users, cross-check against independent sources where possible, and treat silent data errors as serious defects.

• Structured problem-solving under uncertainty. You can decompose ambiguous data discrepancies, form and test hypotheses, and reach defensible conclusions while clearly stating assumptions and outstanding verification needs.

• Strong initiative and persistence. You identify gaps, act without waiting for direction, work through difficult problems, and get up to speed quickly on unfamiliar systems and domains.

• Strong communication skills in both Chinese and English.

Nice to Have

• Experience in crypto, market data, trading systems, or trading operations.

• Strong Java systems-engineering capability, including JVM and concurrency fundamentals, profiling, performance tuning, and reliable high-throughput services.

• Strong Python data-engineering capability, including pandas, Polars, PyArrow, or Spark; experience with vectorisation, memory behaviour, and processing datasets larger than memory.

• Experience delivering tested, packaged Python tooling that other teams depend on.

• Depth in trading-data analysis, such as reconstructing positions from fills, balances from transfers, or PnL across settlement or expiry boundaries.

• Experience reconciling derived trading data against authoritative sources and resolving discrepancies between systems.

• Experience with real-time streaming pipelines, ideally Kafka, including ordering, gap detection, checkpoints, restart recovery, and replay.

• Hands-on experience with time-series or analytical databases, including partitioning, retention, and query-performance tuning at scale.

• Exposure to Apache Iceberg, Parquet, Spark, gRPC, Kubernetes, or cloud data infrastructure.

• Experience building connectors to external REST or WebSocket APIs.

Working Model

• Hong Kong, onsite (in-office).