Chief Data Officer

  • 35749
  • Non-Life - Data Science
  • |
  • New Jersey, United States
  • |
  • Sep 3, 2019
  • Develop and maintain a data model that contains the major entities and attributes which represent the ideal state of content and structure for the vertical. The model should account for both data in its raw state and any transformed/normalized views that would create efficiencies and provide value added transformed attributes.
  • Update model as needed to keep pace with the changing state of both data availability and the business.
  • Review this ideal state with the business and ensure their understanding of why it represents the ideal state for us to be in.
  • Develop and maintain a rolling plan to increasingly populate our data model so our conceptual model is fully realized over time.
  • Identify new or existing raw sources of information to better populate our conceptual data model.
  • Determine most effective means for ingesting data from its source and execute.
  • Define the data engineering and manipulation required to make the data most usable. Specifically, what data will be left in its raw state and for data to be transformed into a more normalized state what the transformation rules are.
  • Provide the highest quality data to the user community by developing and adhering to a data validation process that identifies and corrects data issues at ingestion.
  • Update CEO and CIO quarterly on progress towards full population of the data environment.
  • Publish and maintain a set of technical standards that will define the right tools and how they are to be deployed and used for purpose. At the very least this should include:
  • Data storage and repository tools that account for both structured and unstructured persistent data storage i.e., RDMS and file system techniques.
  • ETL tools required to manipulate data into new structures and formats.
  • Data exploratory tools.
  • Metadata and Master Data Management tools.
  • Data quality and reporting tools.
  • Develop a rolling plan to migrate data environments across company to a more consistent state that adheres to these standards.
  • Update CEO and CIO quarterly on progress and adjustments to plan.
  • Maintain a current census of the data engineering community within the vertical.
  • Develop a minimum set of standards for skills required by a company data engineer.
  • Continually assess the skill level and identify areas of concern/need and pursue proper path to address.
  • Create a rotation process for acceleration of in-flight projects and career advancement.
  • Establish in partnership with the CHRO an ongoing program of recruitment for talent to be made available to business verticals as well as to populate the center’s analytic team.
  • Maintain a current census of all data engineering work within the vertical divided into on-going operational work and new development.
  • On a quarterly basis update CEO and CIO on progress of new development and identify areas of opportunity in both on-going operational work and new development. Make recommendation for how to achieve the opportunity.
  • Build and maintain data engineering team at center to support verticals and provide overflow to BU’s when needed or accelerate high priority work.
  • Determine and recommend budget for data engineering teams to CIO and CEO.
  • Demonstrable progress in defining new data content and advancing population of that content.
  • Data technologies, methods, and skills are efficiently diffusing across the company.
  • Data is in a more usable state and made more available.