Collins McNicholas

The purpose of the Data Analyst is to effectively generate, analyse and present information for key business activities in order to deliver insightful analysis to support key business decisions.

The data analyst will work with senior management to interpret and influence business needs and design analytics-driven strategies, while building and maintaining strong relationships. You will provide a deep understanding of the company’s data, how best to analyse it and create solutions.

Key Responsibilities

  • Involvement in key business projects from beginning to end from a data analytics perspective
  • Involvement in onboarding and offboarding projects, driving the business requirements
  • Carry out detailed quality assurance analysis on all data being onboarded/offboarded
  • Build relationships with different stakeholders and business functions on multiple projects
  • Key point of escalation for data governance and data quality issues
  • Liaise between the business and technology to ensure that data related business requirements are clearly defined, communicated and understood ensuring operational effectiveness.
  • Serve as point of escalation for data quality issues
  • Lead the design and delivery of systems, processes and dashboards for the streamlined production and analysis of quality management information to support management decision making.
  • Develop and implement effective controls and standardised processes.
  • Identify and exploit data for competitive advantage

Skills / Experience

    • SAS or SQL
    • Data warehouse experience
    • Visual Basic Programming
    • Previous experience in financial services
    • Experience as a data quality specialist in large integration projects (e.g. Data Warehouse, Master Data Management
    • 3rd Level Degree – Data Analytics Qualification


    • Exposure to range of asset types (residential mortgages, commercial real estate mortgages and consumer finance products)
    • High level of drive and commitment to personal and business success.
    • Ability to use a wide variety of data sources, structuring data sets and delivering analysis to a high, auditable standard
    • High level of autonomy and self-management


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