Data Engineer

Responsibilities:

Data Quality & Governance: Implement validation checks and governance practices to ensure data accuracy, completeness, and proper metadata and lineage management throughout the data lifecycle.
Continuous Improvement: Identify and propose enhancements to data pipelines, tools, and infrastructure while staying current with emerging trends and technologies in data engineering.
Data Integration & ETL: Develop automated ETL processes to integrate data from diverse sources (databases, APIs, files, streaming), ensuring consistency, accuracy, and readiness for downstream systems.
Technical Collaboration & Support: Collaborate closely with AI, data science, and engineering teams to deliver data solutions and provide technical support aligned with business and analytical needs.
Data Infrastructure Management: Design, build, and maintain scalable data pipelines on both on- premise and cloud platforms (AWS, GCP, Azure), ensuring efficient storage, performance, and data reliability.

Requirements:

Proficient in Python, SQL, Bash; experienced with data storage systems (SQL, NoSQL, data lakes), big data tools (Spark, Hadoop), cloud platforms (AWS, GCP, Azure), CI/CD pipelines, and MLOps practices.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field; Master’s degree is a plus.
3–5 years of hands- on experience in data engineering; proven track record in building high- performance AI/data platforms (IDEs, access control, model deployment, etc.).
Strong analytical thinking, attention to detail, and the ability to work effectively across cross- functional technical teams.

Cập nhật gần nhất lúc: 2026-02-27 00:40:02