SQL Marketing Data Analyst

We are looking for an experienced SQL Data Analyst to support our US partner cllent. The day-to-day tasks will primarily involve pulling and analyzing reports on large data sets. 

This is a great opportunity for someone who has e-commerce experience and wants continuous learning and growth in their career.

Offer: PHP90,000 - PHP 105,000 

The Benefits:

• 100% Work From Home
• Attendance, Performance, and Referral Bonuses
• Paid Holidays and Time Offs
• Health Insurance 
• Continuous Learning and Development 
• Bi-weekly salary payout
• Work-Life Balance

• Write, optimize, debug SQL queries; create joins, aggregations, and subqueries
• Pull and utilize data across Meta, Google, Shopify, Google Sheet/Excel
• Data manipulation (i.e., sorting, scrubbing, tagging, cleaning, de-duping)
• Build reports and dashboards from scratch or using provided templates
• Analyze results and write a summary of reports
• Create reports for partners upon request
• Provide daily/ weekly/ monthly reporting on various performance and marketing data sets
• Validate and troubleshoot existing reports for data/logic accuracy
• Perform other ad-hoc data analysis tasks as needed

• 2-4 years of experience in complex data analysis
• Has experience in SQL SME and autonomous work
• With intermediate to advanced skills in MS Excel/Google Sheets
• Has demonstrated ability in generating ad-hoc reports and dashboards from scratch
• With a strong ability to write, optimize, and debug SQL queries; experience creating joins, aggregations, and subqueries
• With familiarity in general data warehouse infrastructure, specifically with Snowflake, Fivetran, Sigma)
• Experienced in creating/managing dashboards in a BI tool (Sigma is a plus)
• With basic e-commerce knowledge (KPIs such as CAC, ROAS); familiarity with Shopify
• With great English skills (oral and written)
• Familiarity with Slack and Gmail
• With recent experience working in the graveyard shift and amenable to working in PST

• Bonus: Ability to both pull and analyze data + make efficiency suggestions
• Familiar with automation and tech stack integrations; able to suggest and implement automation
• Experience wtih inventory data analysis