Senior Data Engineer
Summary
Fully remote data quality/delivery role (Malaysia) focused on investigating, reconciling, and resolving data issues across large multi-source retail/transactional datasets rather than building pipelines from scratch. Core requirement is strong SQL; Snowflake and dbt are preferred.
Salary: RM 9,000 – RM 13,000 per month
About the Company (Fully Remote)
Our client is a leading data-driven technology organization that processes and manages high volumes of transactional and retail-related data across multiple partners and business channels. The business operates in a fast-paced environment where data accuracy, reliability, and timeliness are critical to supporting both commercial and operational decision-making.
As the organisation continues to scale, they are looking for a hands-on Data Delivery Engineer to help ensure the integrity, quality, and reliability of large data streams flowing through their platform.
Overview of the Role
This is not a traditional Data Engineering role focused on building pipelines from scratch.
The successful candidate will play a critical role in monitoring, investigating, analysing, and resolving complex data issues across multiple data sources and systems. You will work closely with internal teams and external stakeholders to identify data anomalies, determine root causes, and implement sustainable solutions that improve overall data quality and operational performance.
The role requires a strong analytical mindset, curiosity, and a natural ability to troubleshoot data problems from source to destination.
You should enjoy asking questions such as:
Why is this data missing?
Where did this discrepancy originate?
Is the issue caused by source data, business rules, or transformation logic?
How do we prevent this problem from recurring?
Key Responsibilities
Investigate data discrepancies, anomalies, and quality issues across large datasets.
Analyse data flows and trace issues throughout the entire data lifecycle.
Perform root cause analysis and identify whether issues originate from source systems, transformation processes, business rules, or external partners.
Validate, reconcile, and troubleshoot data to ensure accuracy and completeness.
Monitor data quality metrics and proactively identify potential issues.
Work with internal stakeholders and external partners to resolve data-related incidents.
Support production operations by managing and resolving complex data issues.
Implement process improvements and long-term fixes to prevent recurring problems.
Ensure data delivered to the business is accurate, reliable, and fit for purpose.
Contribute to data governance, operational excellence, and continuous improvement initiatives.
Requirements
Essential
4-8 years of experience within Data Operations, Data Quality, Analytics Engineering, Data Support, or similar functions.
Strong SQL skills with experience working on large datasets.
Experience investigating data issues, performing root cause analysis, and resolving data discrepancies.
Hands-on experience with data validation, reconciliation, and quality monitoring.
Strong analytical and problem-solving abilities.
Experience working with complex data environments involving multiple source systems.
Ability to independently investigate issues and drive resolution.
Excellent communication and stakeholder management skills.
Preferred
Experience with Snowflake.
Exposure to dbt or similar transformation frameworks.
Experience supporting production data environments.
Exposure to retail, e-commerce, loyalty, payments, transactional, or high-volume data ecosystems.
Knowledge of data observability, monitoring, or operational support processes.