Data Analyst

Role Purpose

The purpose of this role is to interpret data and transform it into actionable information (such as reports, dashboards, and interactive visualizations) that can enhance business decision-making.

Key Responsibilities

• Project Management: Oversee the technical scope of projects, ensuring alignment with requirements throughout all phases.
• Data Gathering and Analysis: Collect information from diverse sources (data warehouses, databases, etc.) to identify patterns and trends.
• Record Management: Develop and implement processes and policies for effective record management.
• Client Relationship Management: Build and maintain relationships with clients at all levels, understanding their needs and requirements.
• Data Insights and Reporting: Provide sales data, proposals, insights, and account reviews to clients.
• Efficiency and Automation: Identify opportunities to enhance efficiency and automate processes.
• Automated Data Processes: Set up and maintain automated data processes.
• Data Validation: Evaluate and implement external services and tools for data validation and cleansing.
• Performance Tracking: Produce and monitor key performance indicators (KPIs).
• Survey Analysis: Design and analyze surveys based on customer requirements.
• Data Visualization: Create dashboards, graphs, and visualizations to present business performance and conduct sector and competitor benchmarking.
• Predictive Modeling: Develop predictive models and share insights with clients as needed.

Stakeholder Interaction

• Internal: Collaborate with Project Managers and Database Leads for regular reporting and updates.
• External: Engage with clients for reviews and engagement.

Required Competencies

Functional Competencies/Skills:

• Leveraging Technology: Expertise in current and emerging technologies (automation, tools, and systems) to improve efficiency.
• Process Excellence: Ability to adhere to standards and norms for consistent results and risk reduction.
• Technical Knowledge: Proficiency in programming languages and software (Python, Excel, VBA, Matlab, SQL) related to data analytics.

Competency Levels:

• Foundation: Basic understanding with minimal guidance.
• Competent: Demonstrates the competency independently in various situations.
• Expert: Applies the competency universally and guides others.
• Master: Coaches and develops organizational capability in the competency area.

Behavioral Competencies

• Formulation & Prioritization
• Client Centricity
• Execution Excellence
• Passion for Results
• Confidence
• Business Acumen

Performance Metrics

1. Data Analysis and Insights:
   • Measure success through the number of automations implemented, on-time delivery rates, customer satisfaction (CSAT) scores, zero escalations, and data accuracy.
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