- Embed with client teams to scope data and ML problems, map source systems and deliver working outcomes.
- Own delivery quality, including validation, reconciliation to source and stakeholder sign-off.
- Turn repeated client patterns into reusable components for the Tessera platform.
- Design and develop scalable ETL/ELT pipelines using SQL, Python and dbt.
- Design canonical enterprise data models and cross-system mapping rules for master and transactional data.
- Implement validation, reconciliation and monitoring to resolve duplicate, inconsistent and legacy data.
- Engineer features and build models on enterprise data for use cases such as forecasting and anomaly detection.
- Deploy ML models to production and monitor them.
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