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Becoues/README.md
Ziven Liu — Data platforms & applied AI.

I build and operate data platforms, connecting ingestion and lakehouse modeling with business metrics, application data, and AI-assisted analytics.

My work spans system design, implementation, production operations, and the analysis that makes the data useful.

Website & writing   ↗    All repositories   ↗


Selected work

01   Production lakehouse engineering

Build and operate a layered lakehouse with Dagster, dbt, Spark, and Iceberg, turning application and payment data into reusable analytical models.

The work spans incremental processing, partitioned backfills, data quality checks, and production delivery on Kubernetes through GitOps.

DAGSTER   /   DBT   /   SPARK   /   ICEBERG   /   KUBERNETES

02   Change data capture & operational serving

Develop PostgreSQL CDC ingestion and publish warehouse-derived user attributes to application-facing data stores.

Handle snapshot/change ordering, deletion semantics, and event deduplication. Deliver attributes to DynamoDB through validated imports and versioned table cutovers, retaining the previous version for recovery.

POSTGRESQL   /   AWS DMS   /   KINESIS   /   DYNAMODB

03   Subscription metrics & product research

Define subscription, retention, and revenue metrics across billing sources, reconciling currency units, transaction timing, and payment states.

Investigate churn through cohort and behavioral analysis, with complete observation windows and explicit leakage checks. Deliver traceable datasets, dashboards, and written findings for product, finance, and audit work.

SQL   /   DATA MODELING   /   RECONCILIATION   /   COHORT ANALYSIS

04   Governed analytics & AI access

Deploy and extend Superset for shared analytics, with MCP access tied to individual user identities and existing role-based permissions.

Implement credential issuance, expiry, and revocation, and validate identity isolation across concurrent requests. Agent access follows the same Superset permissions as interactive use.

SUPERSET   /   MCP   /   RBAC   /   PYTHON   /   ARGO CD


Engineering focus

Area What I work with
Data platforms Python, SQL, Dagster, dbt, Spark, Iceberg · orchestration, incremental models, data quality
Ingestion & serving PostgreSQL, DMS, Kinesis, DynamoDB · change capture, state reconstruction, application data delivery
Analytics & AI Superset, MCP, cohort analysis · metric definitions, reconciliation, governed data access
Platform operations Kubernetes, Helm, Argo CD, GitHub Actions · GitOps, deployment checks, environment isolation

Notes & experiments

I write about data systems and share what I learn while building. Explore the notes and experiments on my personal site.



Based in Shanghai   ·   Building across data, AI, and the interface between them.

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