Data Modeler (DM) Description Summary: The DM will report to the Manager, Data and will work within an Agile scrum team.
The DM works closely with its internal customers to define and implement structured data systems, ETL flows and Business Intelligence (BI) content for consumption by end-customers within the company's platform.
The DM cooperates with our Technical Product Managers (TPM) role to interview analysts and gather their requirements.
He/she/they work closely with a Data Analyst and Software Engineer to ensure that the developed data solutions align both with the TPM's requirements and Engineered solutions.
The DM embodies knowledge of the Platform's data systems and associated ETL flows and data visualizations.
The DM delivers value to end customers by giving a business analyst a means of bringing their data product to market.
The DM provides stewardship of the Platform's data concerns.
The DM ensures that technical debt is identified, documented and advocated for as/when appropriate.
Solutions that the DM develops may first require a feasibility study or Proof-of-Concept (PoC) be executed by the DM, the results of which the DM will share with all stakeholders.
Existing and potential areas of work: • Data Modeling o Designing and implementing data models embodied in structured files and data stores • ETL o Extract, Transform and Load of any data relevant to the Platform o Monitoring and bug fixes in PROD • BI o Data visualizations and inter or intra-navigation • Data o Data Science, Machine Learning o Search optimization o Structured data systems o Unstructured data systems o Data Caching o Data persistence o Data recovery • Co-interview analysts, in cooperation with a TPM, to identify achievable scopes of work (SOWs) of business value • Co-author data model SOWs with business analysts and TPMs • Breakdown data model SOWs into actionable Tasks and size those Tasks • Develop VB data interchanges with data stores for use in MS Excel • Write and maintain ETL dataflows that move data between stores or into new formats • Write user guide documentation for analysts who use the data models produced by the team • Interface with external data source(s) and/or vendor(s) to store new model data in an ETL target db • Document all ETL flows and interconnections in Confluence • Cost ETL solutions • Build and retain all data intellectual property for the Platform • Provide end-customer technical support during high-severity platform situations • Monitor and debug dataflows in PROD • Coordinate with the Engineering and Architecture teams, providing information on technical investigations into buy/partner decision options, and make associated recommendations None
