Senior Reporting and Data Analyst

Main Objective:

The primary objective of the Senior Reporting and Data Analyst is to drive data analysis and reporting processes within the organization, ensuring data quality, accuracy, and effective communication of insights to stakeholders. The successful candidate will begin as a hands-on contributor, taking full responsibility for delivering accurate, insightful data and communicating findings to stake holders. Upon demonstrating exceptional ability and a strategic understanding of the business, this role will transition into a leadership position, where the individual will be responsible for building, developing and managing a team of data analysts.

This role requires advanced data analysis and interpretation skills, a deep appreciation and understanding for our business, and an insight-driven approach to strategy formulation to identify the right numbers to report on and analyze, ultimately serving as the basis for strategies to optimize operations.

You will work directly with the Support Manager to drive profitability and operational efficiency through well-established industry best practices (i.e. LEAN and AGILE)

Key Responsibilities:

• Strategic Leadership and Planning:
  • Define and implement data analytics strategies aligned with business goals.
  • Develop and maintain data governance policies and procedures.
  • Establish data quality standards and ensure their implementation.
• Team Management and Development:
  • Build a high-performing team with strong data analysis and reporting skills.
  • Lead and mentor the team, providing guidance and support.
  • Foster a culture of continuous learning and development within the team.
• Data Analysis and Reporting:
  • Oversee the collection, cleaning, and analysis of data from various sources.
  • Identify patterns, trends, and anomalies in data using statistical techniques and tools.
  • Develop and maintain reports and dashboards to communicate data insights effectively.
  • Ensure the accuracy, reliability, and timeliness of data analysis and reporting.
• Collaboration and Communication:
  • Collaborate with other departments and stakeholders to understand their reporting needs.
  • Effectively communicate clear and concise data insights to management and other stakeholders
  • Present findings and recommendations in a compelling way that drives action.
• Continuous Improvement:
  • Stay up-to-date with industry trends and best practices in data analytics and reporting.
  • Identify opportunities for process improvement and automation in data analysis and reporting.
  • Implement and monitor data quality and performance metrics.

Key Performance Indicators (KPIs):

• Data Quality and Accuracy:
  • Data Accuracy Rate:
    • The percentage of data entries without errors, ensuring reliable insights.
  • Data Quality Score:
    • An overall assessment of data based on accuracy, completeness, and relevancy.
  • Data Completeness:
    • Measures the percentage of data fields that are filled in, ensuring a comprehensive view.
  • Data Consistency:
    • Ensures data is standardized and adheres to established rules, preventing inconsistencies.
• Data Utilization and Impact:
  • Percentage of Data-Driven Recommendations Implemented
    • Measures the adoption and impact of the insights generated from the reports produced by the team.
  • Quantifiable Impact of Optimization Strategies (e.g., % Cost Reduction, % Efficiency Gain, % change in GM)
    • link data insights to business outcomes.
  • Stakeholder Satisfaction with Insights (e.g., Survey Score)
    • Measures how well the insights are understood, valued, and used by management and other stakeholders; with a special focus on how well specialists receive the scorecard and other reports
• Reporting and Delivery:
  • Time to Generate Reports:
    • Measures the speed at which reports are produced, ensuring timely insights for decision-making.
  • Report Production Cycle Time:
    • Measures the total time it takes to complete a report, including data collection, analysis, and delivery.
• Performance and Efficiency:
  • Average Database Uptime:
    • Indicates the reliability of data infrastructure, ensuring consistent access to data for analysis.
  • Number of Errors and Incidents:
    • Tracks the frequency of errors and incidents, highlighting areas for improvement in data processes.
  • Turnaround Time for Fixing Errors and Incidents:
    • Measures how quickly errors are resolved, ensuring minimal disruption to reporting processes.
  • Cross-Functional Relevance Score
    • Feedback from other departments on the data team’s responsiveness, collaboration, and contribution to their objectives.

Required Qualifications & Skills

• Technical Skills:
  • Data Analysis: Expertise in data manipulation, analysis, and visualization using tools like Excel, SQL,Google Looker Studio & Power BI
  • Statistical Analysis: Strong foundation in statistical methods and modeling.
  • Programming: Demonstrated ability to write basic scripts using languages like Python and/or R for data analysis and automation Proficiency in programming languages like SQL, Google AppScript, Java, Visual Basic, Python and/or R is definitely a plus.
  • Database Management: Familiarity with database management systems and data warehousing concepts.
• Analytical Skills:
  • Critical Thinking: Ability to analyze complex data, identify patterns, and draw meaningful conclusions.
  • Problem-solving: Ability to identify and solve problems using data analysis techniques.
  • Attention to Detail: Ensuring data accuracy and integrity throughout the analysis process.
• Leadership & Communication Skills:
  • Leadership: Ability to lead and motivate teams, set goals, and manage projects.
  • Communication: Excellent communication skills to present findings clearly to stakeholders.
  • Collaboration: Ability to work effectively in teams and with interdisciplinary groups.

Find out more about Civicom Pacific at www.civi.com and our Feathers Project at www.feathersproject.org.