Data Engineer

The Data Engineer is responsible for designing, building, and maintaining the company’s data infrastructure — from raw data ingestion through to production-ready datasets that serve both operational systems and AI workloads. A core focus of this role is building a comprehensive metadata and semantic layer that clearly describes every dataset, table, and field, enabling AI systems to scan, analyze, and generate reports autonomously.

Responsibilities

• This role works closely with the technical team, AI team, project team, and clients to ensure data is reliable, well-documented, and ready for both human analysis and machine consumption.
• The Role Data Pipeline Development Design and build automated ETL/ELT pipelines to ingest data from multiple sources (Call Center, DMS, Social Platform, third-party APIs).
• Develop scheduling, monitoring, and alerting mechanisms to ensure pipeline reliability and SLA compliance.
• Implement incremental processing, deduplication, and change data capture (CDC) strategies for high-volume data flows.
• Maintain and optimize existing pipelines for performance, cost, and maintainability.
• Metadata & Semantic Layer Build and maintain a comprehensive metadata catalog with clear descriptions for every table and column across all datasets.
• Design and implement a semantic layer that defines dataset purposes, field definitions, data types, business logic, and relationships between tables.
• Ensure the semantic layer is structured for AI consumption — enabling AI systems to understand data context, scan datasets, perform analysis, and generate reports without manual intervention.
• Establish and enforce documentation standards so that every new dataset or schema change is accompanied by complete metadata.
• Collaborate with the AI team to validate that the semantic layer provides sufficient context for automated data exploration and insight generation.
• Data Quality & Governance Implement automated data quality checks at each pipeline stage: completeness, accuracy, consistency, and freshness.
• Build monitoring dashboards to track data health metrics and alert on anomalies or pipeline failures.
• Establish data lineage tracking so that downstream consumers (reports, dashboards, AI models) can trace data to its source.
• Collaborate with stakeholders to define data ownership, access policies, and retention rules.
• AI Data Infrastructure Prepare and serve datasets for AI/ML workloads: feature stores, training datasets, and inference data feeds.
• Collaborate with the AI team to define data requirements for model training, evaluation, and monitoring.
• Build and maintain data APIs or serving layers that enable AI systems to consume data in real time or near-real time.
• Support the AI team in data profiling, exploratory analysis, and data readiness assessments for new AI use cases.
• Cross-team Collaboration Work with the technical team on system integration and data flow architecture across platforms.
• Collaborate with the AI team to ensure data infrastructure meets the requirements of AI/ML workloads.
• Support the project team and clients in understanding data availability, structure, and operational workflows.
• Participate in client-facing discussions to gather data requirements and provide technical guidance on data solutions.

Location

Ho Chi Minh City Brand: Merkle Time Type: Full time Contract Type: Fixed Term Contract (Fixed Term)