Data Engineer

Responsibilities and Duties
1) Source Systems Integration
Design and maintain reliable data ingestion and integration from multiple source systems, including:
ERP platforms.
SAP systems.
CRM and digital platforms.
Work with APIs, file-based integrations, and database integrations to ensure consistency and scalability.
2) Data Platform Engineering (Storage / Processing - Microsoft Stack)
Design data storage layers following raw/curated/serving principles on Data Lake/Lakehouse platforms (ADLS Gen2 / OneLake) to support analytics and lineage/traceability.
Develop transformations and processing using SQL and/or Spark on the appropriate compute layer (Fabric / Synapse / Databricks), optimizing for incremental patterns when feasible.
Organize analytics serving layers (Lakehouse/Warehouse) for stable and performant access by BI/Analytics users (Fabric Warehouse/Lakehouse or Synapse SQL).
3) Data Modeling & Serving
Develop and maintain data models for analytics and reporting (dimensional modeling, star schema, domain data marts).
Collaborate with Data Analysts/BI teams to standardize schemas, metrics, and data consumption patterns.
4) Data Quality, Governance & Security
Implement and maintain data quality validations and basic governance standards (naming conventions, dataset ownership, etc.).
Apply best practices for data security, compliance, and access control (least privilege, auditability, access governance).
5) Operational Ownership (Production)
Build production-ready pipelines with robust error handling and support for rerun/backfill without duplicating or corrupting data.
Set up observability for pipelines and datasets, including logging, basic metrics (freshness/volume/error/latency), and appropriate alerting to detect issues early.
Participate in data incident handling: investigate root causes, coordinate with stakeholders (source owners, BI, infrastructure), and document improvements to prevent recurrence.
Handle common operational scenarios such as late-arriving data, schema changes, retries, and reprocessing/backfill to ensure analytics data is correct and delivered on time.
6) Phối hợp và Triển khai / Collaboration & Delivery
Work closely with Data Analysts, BI teams, and business stakeholders to understand needs and deliver the right outputs.
Support both batch and real-time/near real-time requirements depending on system needs.
Contribute to DevOps/ DataOps practices for data lifecycle management (release, operations, continuous improvement).