KEY RESPONSIBILITIES

Data Analysis & Operations

• Write, optimize, and maintain complex SQL queries on Customer Data Platform (CDP) to extract, transform, and analyse large datasets for business insights and decision-making.
• Handle ad-hoc data extraction and perform exploratory analysis to proactively recommend data-driven improvements.
• Support CRM team in target segmentation, campaign setup, performance tracking, and identifying opportunities to optimize loyalty and marketing programs.

BI & Reporting

• Develop and maintain automated dashboards and reports using BI tools (e.g., Power BI, Tableau, Looker, or equivalent).
• Develop reporting automation solutions using Power Automate, Python, or similar tools to support reporting requirements beyond the capabilities of BI platforms.
• Maintain & optimize reporting pipelines to ensure reliability, and scalability.

Automation

• Design, develop, and maintain ETL pipelines and automated workflows to ensure reliable, efficient, and stable day-to-day data operations.
• Create automation solutions for recurring ad-hoc requests to streamline business processes and improve operational efficiency.

Collaboration & Communication

• Work closely with the business to translate reporting requirements into robust data pipelines and dashboards.
• Prepare clear presentations, communicate data insights to stakeholders, and effectively address questions to support data-driven decision-making.
• Collaborate with IT and data teams to improve data infrastructure performance and ensure data availability.

QUALIFICATIONS & SKILLS

Required

• 2+ years of experience in data analytics, business intelligence, or related roles.
• Strong proficiency in SQL and working with large, complex datasets.
• Hands-on experience with at least one BI tool (Power BI is preferred).
• Experience using Python or R for data processing, automation, or analysis.
• Strong analytical and problem-solving skills.

Preferred

• Experience working with on-premises data warehouses (e.g., PostgreSQL).
• Be able to work with ETL/ELT processes, data pipelines, or data modelling.
• Exposure to data engineering practices and large-scale data infrastructure.