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.
