Role Main Purpose:
Data Analyst plays a critical role in leveraging data to drive business decisions by analyzing, interpreting, and visualizing data for various corporate functions. The role is responsible for extracting insights from structured and unstructured data, creating dashboards, and supporting decision-making with data-driven recommendations. The Data Analyst works closely with business stakeholders, data engineers, and business intelligence teams to transform raw data into actionable insights. The role requires expertise in data analytics, statistical modelling, visualization, and reporting tools to improve business performance.
Responsibilities:
- Data Extraction & Preparation
- Collect and analyze data from multiple sources, including databases, ERP, CRM, HRMS, and systems.
- Use SQL, Python, or R to extract and manipulate structured and unstructured data.
- Clean, transform, and validate data to ensure accuracy, completeness, and consistency.
- Collaborate with data engineers to optimize data pipelines and storage for analysis.
- Business Intelligence & Reporting
- Develop interactive dashboards and reports using Power BI, or other platforms.
- Create automated reports to provide real-time business insights.
- Present data insights to business leaders, finance teams, HR, marketing, and operations.
- Provide trend analysis, forecasting, and KPI tracking to support strategic decision-making.
- Predictive Analytics & Statistical Modelling
- Perform statistical analysis and hypothesis testing to identify patterns and trends.
- Use predictive analytics to forecast revenue, customer behavior, operational performance and other KPI relevant to the business needs of SPL
- Implement data segmentation and clustering techniques to improve customer targeting and retention.
- Data-Driven Decision Support
- Work closely with finance, content, marketing, HR, operations and other teams to translate business needs into data solutions.
- Identify opportunities for business process optimization and cost reduction using data insights.
- Provide ad-hoc analysis for business teams to answer key questions and improve performance.
- Performance Monitoring & Optimization
- Monitor key performance indicators (KPIs) and provide insights on business trends.
- Identify data discrepancies, errors, and inefficiencies in reporting processes.
- Work with IT and business leaders to optimize reporting workflows and data management practices.
- Data Security & Compliance
- Security Measures: Work with the Data Protection and cybersecurity to implement PDPL compliance to protect the application stack from threats.
- Access Control: Managing who can access the application and what they can do.
- Conduct regular data integrity checks and audits to maintain accuracy and reliability.
- Maintaining data integrity:
- Data Error Checking: Regularly verifying data to identify and correct errors.
- Validation Procedures: Ensuring data meets specific criteria before it's used.
- Application Integration
- Integrate sports data platforms with ERP, CRM, and other systems to ensure seamless data flow.
- Maintain API lifecycle management for data-sharing across internal and external stakeholders.
- Data Lifecycle Management & Archiving
- Data Migration: Ensuring data is securely transferred to new systems
- Assess Impact: Evaluate the impact of retiring these applications on business operations and data integrity.
- Data Extraction and Transformation: Thoroughly examine and extract all necessary data from the application.
- Data Archiving: Coherently and accessibility of data archive, ensuring it includes necessary metadata for future reference.
- Maintain Data: Establish retention policies to manage the archived data according to business and regulatory requirements.
Best Practice:
- Data Accuracy: Ensure all reports and analytics maintain high accuracy, integrity, and reliability.
- Standardization: Follow best practices in data governance, metadata management, and reporting frameworks.
- Automation: Reduce manual effort by implementing automated reporting and alert systems.
- Security & Compliance: Maintain confidentiality and compliance with corporate data policies.
- Continuous Learning: Stay up-to-updated with industry trends, new tools, and best practices in analytics.
Education & Experience:
- 3-5 years of experience in data analysis, business intelligence, or statistical analysis.
- Strong expertise in data visualization, dashboard creation, and business reporting.
- Bachelor’s or master’s degree in data science, Statistics, Computer Science, Business Analytics, or a related field.
- Certifications in Power BI, Tableau, SQL, CDMP or Google Data Analytics (Preferred).
Knowledge & Skills:
- Low ego - Go-getter & Can-do attitude
- Ability to build relationships based on transparency
- Adopt, Apply and promote the SPL culture and values (Performance - Ambition - Governance – Professionalism)
- Ability to transfer confidence to team members
- Ability to transfer knowledge and experience to team members
- Promote Openness and clarity
- Ability to manage and develop people
- Ability to delegate
- Problem-solving skills
- Decision making
- Effective and clear communication
Technical Skills:
- Programming & Querying: Proficiency in SQL, Python, R, or Excel (advanced functions, macros, VBA).
- Data Visualisation: Hands-on experience with Power BI, Tableau, Looker, Qlik, or similar BI tools.
- Databases: Familiarity with relational (SQL Server, PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) databases.
- Statistical Analysis: Experience in A/B testing, regression analysis, clustering, and forecasting models.
- Data Integration: Ability to work with API-driven data sources and cloud data warehouses (AWS, Azure, GCP).
- Machine Learning Exposure (Preferred): Understanding of basic ML models, predictive analytics, and AI-driven insights.
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