Data Analyst - Mandaluyong

By embedding data-driven insights into day-to-day execution, the Operations Data Analyst will help streamline processes and improve our speed and effectiveness across core functions.

The ideal candidate brings a strong foundation in data analysis, paired with a practical understanding of operations. They will thrive in a high-growth, execution-focused environment and demonstrate the ability to translate raw data into actionable intelligence for continuous improvement.

Key Responsibilities:

• Build and maintain dashboards that track operational KPIs, including time-to-fund, cycle times, renovation milestones, and agent productivity.
• Develop scalable and automated reporting frameworks to support leadership’s data-driven decision-making.
• Monitor data trends and create real-time insights that flag performance issues or operational inefficiencies.

• Conduct root cause analyses on delays or bottlenecks, and recommend targeted process improvements.
• Produce recurring and ad hoc reports to assess performance across the lifecycle—from agent activation and construction management to financing flows.

• Work closely with field teams, sales, construction, and finance to surface key performance insights and support targeted interventions.
• Contribute to the refinement of onboarding processes for brokers and agents, balancing speed, compliance, and effectiveness.

• Collaborate with engineering and data teams to support the maintenance and optimization of data pipelines and analytics infrastructure.
• Ensure high data quality and integrity across operational databases and dashboards.

Qualifications:

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field.
• Minimum 3 years of experience in data analytics, business intelligence, or a related operations-focused role.
• Proficiency in SQL and working with relational databases.
• Strong command of data visualization tools (e.g., Tableau, Power BI, Looker) and advanced Excel.
• Demonstrated ability to distill complex datasets into clear insights and decision-support outputs.
• Strong critical thinking, attention to detail, and results-oriented mindset.
• Excellent verbal and written communication skills, with an ability to work cross-functionally.