Cloud Data Engineering Lead / Senior Data Engineer at Qode

Ho Chi Minh City, , Vietnam -

Full Time

Start Date

Immediate

Expiry Date

23 Jun, 26

Salary

0.0

Posted On

25 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Python, AWS, GCP, Azure, Airflow, DBT, Glue, Spark, Data Modeling, ETL, ELT, Data Lake, Data Warehouse, Lakehouse, Leadership

Industry

Software Development

Description

This is a position we're hiring for on behalf of our client. 🚀 Company IntroductionOur client empowers organizations by transitioning legacy workloads to cutting-edge Cloud, DevOps, and AI technologies. As Vietnam's AWS Partner of the Year for two years in a row, they bring deep expertise and innovative thinking to every project.They're not just a consultancy, they're a transformation partner. Their dynamic team of engineers, architects, and DevOps experts is passionate about delivering impactful cloud solutions across industries, including FinTech, BFSI, and Digital Startups. 🧠 About the RoleWe are looking for an experienced Data Engineering Lead / Senior Data Engineer who goes beyond building pipelines and understands how data drives business decisions and analytics.In this role, you will: Lead data engineering initiatives Shape data models and analytics foundations Work closely with business stakeholders and customers Guide a team of data engineers This role is ideal for someone who is ready to step up, take the lead, and own both the technical and business-facing aspects of a fast-growing data practice. 📌 Key Responsibilities Data & Analytics LeadershipOwn the design of analytics-ready data models aligned with business KPIs and use cases.Translate business and customer requirements into scalable data solutions.Partner with Product, Analytics, and Business teams to define metrics, reporting structures, and data standards. Data Engineering & ArchitectureParticipating in and building modern cloud-based data platforms (Data Lake, Data Warehouse, Lakehouse).Develop and maintain robust ETL/ELT pipelines using tools such as Airflow, DBT, Glue, Spark, etc.Ensure data quality, reliability, and performance across pipelines and datasets.Support real-time or near–real-time data processing when required. Leadership & Stakeholder EngagementLead and mentor data engineers, providing technical guidance and code reviews.Act as a technical point of contact for customers, explaining data solutions and trade-offs.Collaborate with Cloud Architects, DevOps, and AI teams to deliver end-to-end solutions.Contribute to project planning, estimation, and delivery ownership. 🎯 What You BringMust-Have:6+ years of experience in data engineering or related roles.Strong background as a hands-on data engineer (ETL, data modeling, analytics datasets).Experience designing data models for analytics and reporting, not just raw ingestion.Proficient in SQL and Python.Hands-on experience with cloud platforms (AWS preferred; GCP/Azure acceptable).Experience with orchestration and transformation tools (Airflow, DBT, Glue, etc.).Experience leading projects or acting as a technical lead (formal title not required).Strong communication skills and ability to work directly with business stakeholders or customers. Nice-to-Have:Experience working with BI or Analytics teams (Looker, Power BI, Tableau, etc.).Exposure to AI/ML data pipelines or analytics use cases.Consulting or customer-facing experience.Cloud certifications (AWS Data Analytics, Solutions Architect, etc.). 🎁 What We Offer13-month salaryProject-based bonusWork with cutting-edge cloud & AI technologies.Join global projects with real impact across industries.Modern, dynamic, and multicultural work environment.Clear career path and learning support with access to certificationsFlexible working environment and collaborative team culture.Annual company trips, fun team events, and great office vibes.Competitive salary and full benefits package.

Responsibilities

The role involves leading data engineering initiatives, owning the design of analytics-ready data models aligned with business KPIs, and translating requirements into scalable data solutions. Key tasks include developing robust ETL/ELT pipelines and mentoring a team of data engineers.