Senior Data Architect – Business Intelligence & Analytics 

Reports To: CTO | Revision Date: March 2024 | Department: Data Analytics

Key Responsibilities

  • Data Integration and Management: Responsible for connecting and integrating Mortgage Automator’s backend system with platforms such as Power BI or Tableau.
    This includes being the architect, setting up data pipelines, ensuring data quality, and managing databases

  • Reporting and Visualization: Creating dynamic reports and dashboards tailored to different departments’ needs. This involves understanding departmental goals and translating them into actionable insights through visualizations.
  • Analytics and Insights: Analyzing data to uncover trends, patterns, and insights that can inform business strategy and operational decisions. This might involve predictive analytics, customer segmentation, and performance analysis.
  • Collaboration and Consultation: Working closely with Senior Leadership and department heads to identify their data needs, providing consultancy on how to best leverage data, and training users on interpreting and using BI tools like Power BI or Tableau.
  • Data Governance and Security: Ensuring that data handling and analytics practices comply with relevant data protection regulations and company policies. Knowledge of data privacy laws (such as GDPR, CCPA).

Skills and Qualifications

  • Technical Skills: Proficiency in data analytics and visualization tools (Power BI and/or Tableau specifically), experience with SQL and data warehousing, and knowledge of ETL processes.
  • Power BI/Tableau Dashboards: Experience designing and implementing dashboards in Power BI/Tableau that provide actionable insights and support business decisions.
  • CRM and Operational Tools Expertise: Experience across a wide array of CRM, LMS, LOS and other Client systems, coupled with project management collaboration tools such as Jira, Confluence and related.
  • Data Integration Projects: Involvement in projects that required integrating data from various sources into a unified database or data warehouse.
  • Analytics Reports: Creation of detailed reports analyzing business performance, customer behavior, sales trends, etc., which led to actionable business strategies.
  • Predictive Modeling: Projects that involved using statistical models to predict future trends or outcomes based on historical data. With the ability to translate and articulate in a consumable manner to the broader team.

Specific Experience:

  • Client Facing Models: Development of models to calculate the lifetime value of customers (CLTV), Churn reporting and predictive Churn models, Customer Sentiment, Revenue enablement velocity models. Reporting and models to enable Client Facing targeted strategies (including marketing and sales) to maximize client satisfaction and profitability.
  • Operational Efficiency Analysis: Projects focused on analyzing operational data to identify bottlenecks and inefficiencies. Implementation of analytics-driven recommendations to streamline processes, reduce costs, and improve service delivery.
  • Risk Management Dashboards: Creation of comprehensive dashboards for risk management, incorporating data from various sources to provide real-time insights into operational risk.
  • AI Integration: Leveraged AI in the automation of data analysis, improve predictive analytics, and enhance decision-making processes. This includes developing models that can predict risk, optimize touch-points and personalize customer experiences. Development of AI-driven tools, such as chatbots, that use customer data to provide personalized assistance, reduce response times, and improve customer satisfaction.

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