Analytics Engineer - MY877841
Summary
Analytics engineer on a consulting team serving retail clients: resolve ~20 data tickets per week, run end-to-end SQL spike analyses, and build retailer-specific dbt data models on Snowflake within weekly sprints. Remote from Kuala Lumpur; open to local Malaysian candidates only.
Analytics Engineer
Working Arrangement: KL (Remote)
Eligibility: Local Malaysians only (Unable to sponsor visa)
Role Overview
We are seeking a detail-oriented and proactive Analytics Engineer to join our client's high-performing data team. In this role, you will be responsible for handling end-to-end data ticket resolution, performing exploratory analytical spikes, maintaining high data accuracy, and supporting retailer-specific data models.
You will work closely with team leads in daily stand-ups to prioritize tasks, filter out data noise, and deliver accurate, business-critical outputs for our clients.
Key Responsibilities
Ticket Management & Problem Solving:
- Manage and resolve operational/support data tickets (typically around 20 tickets per week, depending on sprint volume).
- Perform end-to-end spike analysis on incoming queries and data discrepancies using SQL.
- Proactively identify data inconsistencies and implement fixes to ensure accurate, reliable data delivery.
- Group, prioritize, test, and deploy fixes efficiently within weekly sprint cycles.
Data Modeling & Analytics:
- Build and maintain tailored data models using dbt specifically for retail clients (focused on retailer-specific models rather than global architecture).
- Filter and make sense of complex dataset noise to surface actionable business insights.
- Work on analytical tasks at scale, ensuring high data fidelity on behalf of clients.
Team Collaboration & Workflow:
- Participate in daily stand-ups to align on ticket distribution, priorities, and accurate delivery timelines.
- Collaborate with the broader Data Engineering team (who handle core underlying pipeline architecture).
Requirements & Qualifications
- Advanced proficiency in SQL for data extraction, querying, and end-to-end troubleshooting.
- Hands-on experience or familiarity with dbt and Snowflake (prior experience in large enterprise or backend analytical environments is a plus).
- Strong ability to critically analyze large datasets, distinguish high-value data from noise, and accurately interpret financial/retail data patterns.
- Experience handling high-volume tickets and spike queries in an agile/sprint environment.