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Big Data Engineer
1 week ago
Job Description:
Big Data Engineering
Create, build and maintain the data infrastructure required for Extraction, Transformation and Loading of data from a wide variety of sources.
Ensure high-quality data is delivered to support Compliance / FCC model development.Support the implementation of Data Analytics / Automation for Compliance teams and Business stakeholders
Liaise with relevant stakeholders (e.g., Data Management Office) to identify and assess suitability of data for Compliance / FCC Data models.
Liaise with Business stakeholders / analysts and ensure assembled data assets used for model development meet business requirements.Build & maintain data dictionaries / data requirements of implemented data models and coordinate with Data Scientist(s) in developing model narratives.
Monitor the execution & performance of data models and liaise with IT for any technical / system outagesKeep track of datasets / tables in the working schema and support purging of unused tables periodically.
Perform data analysis required to troubleshoot data related issues and support the resolution of raised data issues.
Support model maintenance
Liaise with Business Analyst to receive and understand business feedback on model performance and incorporate feedback into models
Re-train and recalibrate existing FCC analytical models to prevent model drift periodically or as needed
New model development
Work closely with model end-users and other key stakeholders (e.g., Head of FCC Analytics, Business Analyst, Data and Ops Engineer, GC) to identify additional areas which require analytics support or future model build and include those models in development pipeline
Develop model narratives (e.g., purpose, logic, parameters, data requirements, output surfacing / structuring) in collaboration with the business and other relevant stakeholders
Work closely with other Data Scientist(s) and Business Analyst, and undertake the end-to-end FCC model development, including data wrangling, exploratory data analysis, feature selection, model selection, training, testing, etc.
Build a range of models (rule-based, supervised / unsupervised models, etc.) on structured, semi-structured, and/or unstructured data if needed-
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