Data Governance Lead

  • 39017
  • Non-Life - Data Science
  • |
  • United States
  • |
  • Feb 1, 2021
Insurance
RESPONSIBILITIES
  • Leads Data Governance practice, including guidance, facilitation, and organizational adoption.
  • Coordinates activities and processes of Data Council/Steward Community assuring model is operating according to company policy
  • Provides oversight of the Metadata repository. Partners with technology teams to ensure Data lineage is captured and made available to the organization.
  • Assures execution of governance processes at the enterprise level, including data stewardship, data quality management and metadata management.
  • Collaborates with Data Council, Business Data Stewards, Data security and IT leadership teams to establish data policies and standards for upholding of data quality and data security.
  • Assures standards for information usage, access, and external exposure in concert with enterprise information security
  • Coordinates the development of Data Governance Playbook/Processes in concert with Council/Stewards
  • Trains data stewards and ensures that effective stewardship processes exist
  • Oversee enterprise implementation of common business language and associated meta-data management
  • Work with stewards to maintain metadata definitions and quality thresholds
  • Partner with cross functional Business Data stewards to establish Data Quality thresholds for completeness, accuracy, timeliness and overall data maturity
  • Provides organizational education and awareness of governance model, activities, progress and methods
QUALIFICATIONS
  • Command of data governance and data management best practices, tools and disciplines
  • Experience working with Data Governance tools: Informatica Axon, EDC, EDP, IDQ
  • Proficient in the following data skills/techniques - Business Process Modeling, Data Profiling, Metadata management, Data Lineage, Data Security/Privacy, Master Data Management,
  • Advanced knowledge of Data Warehouse/ Business Intelligence and Analytics project lifecycle (SDLC), tools, technologies, and best practices.
  • Knowledge of technical infrastructure, including cloud environments.
  • Knowledge of Data mining, data lakes, as well as structured/unstructured data concepts.
  • Ability to learn new software, data sources, and skills to improve data management and analytics delivery.
  • Possesses data analysis skills through use of automated tools and mathematical analyses.
  • Knowledge of conceptual, logical and/or physical data modeling.
  • Understands system/data security mechanisms required for internal/external presentation of client analytics via the Internet.
  • Excellent in the use of the Microsoft Office Suite of Tools.
  • Possesses strong capabilities with respect to data visualization, and usability.