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. 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.
Job Description:
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)
