Senior Data Engineering
Summary
Senior Data Engineer builds and maintains a Databricks-based lakehouse for a financial trading business, owning ELT pipelines, real-time streaming, and dimensional modeling to power analytics and compliance.
Responsibilities
- Own the data-warehouse architecture and ELT pipelines for financial trading business; build a layered model (ODS / DWD / DWS / ADS) on Databricks Lakehouse.
- Lead subject-area modeling for customer, account, order, trade, position, clearing & settlement and market data; productize reusable metrics and customer profiles.
- Build the real-time computing stack on Kafka + Flink / Spark Structured Streaming — CDC ingestion and low-latency pipelines powering real-time trade monitoring and live metrics.
- Drive the migration from classic batch warehousing to a lakehouse (Delta Lake / Iceberg) — unified streaming + batch writes, consistency, rollback and schema evolution.
- Define and operate the bar for data quality, metadata, lineage, SLA and change-management; guard warehouse and pipeline (incl. streaming) reliability.
- Partner deeply with trading, product, finance and compliance to land a single source of truth and self-service consumption.
Requirements
- Bachelor's or above in CS / Software / Math / Statistics; 8+ years in DW / big-data engineering.
- Solid financial-industry background: 3+ years building warehouses in securities, exchanges, brokerage, payments or banking; exchange-business data experience (orders, trades, positions, clearing & settlement, market data) preferred.
- Expert in Kimball dimensional modeling, SCD and metric-layer design.
- Hands-on with Databricks (Delta Lake, Unity Catalog, Spark SQL / PySpark, DLT).
- Production experience in real-time computing: expert in Flink or Spark Structured Streaming, with Kafka, CDC (e.g. Debezium), exactly-once semantics and state management.
- Familiar with mainstream open-source big-data frameworks in production — Spark / Flink / Kafka / Iceberg / Hudi / Airflow / DolphinScheduler — able to make technology choices and debug at the framework level.
- Deep SQL & Spark tuning — partitioning, Z-Order, shuffle, broadcast, AQE.
- Familiar with governance tooling (DataHub / Atlas / Data Map).
- Strong cross-functional, upward-communication and written-spec skills.
- Able to use chinese and english as daily working language to work wit chinese speaking stakeholders