Engineer large-scale financial data systems with point-in-time correctness, bitemporal storage, streaming pipelines, data quality controls and governed access.
Duration10 weeks
LevelAdvanced
Modules10
Lessons40
Research and production systems are only as trustworthy as the data beneath them. This course covers advanced data engineering for finance: storage formats and engines, streaming with Kafka, bitemporal modeling for point-in-time research, security master data, orchestration and lineage. You will build a data platform that produces reproducible, look-ahead-free datasets and enforces quality and governance rules.
10 modules · 40 lessons · 10 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build a bitemporal fundamentals store that supports as-of queries without look-ahead
Implement a Kafka streaming pipeline with replay, validation and dead-letter handling
Design a security master with symbology mapping and corporate action adjustments
Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.
7 weeks7 modulesBuild batch and streaming market data pipelines in Python with Kafka, PostgreSQL and Parquet, covering normalization, bar aggregation, quality checks and replay.
10 weeks10 modulesStudy and build low-latency market data systems: feed handlers, order book reconstruction, efficient serialization, lock-free design, measurement and resilient distribution.
5 weeks5 modulesLearn the foundations of financial data engineering: data types, schemas, SQL, ingestion, cleaning, corporate actions and reliable batch pipelines in Python.
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