Cloud Data Engineering Lead

Qode
Full_time•Vietnam, Uganda

📍 Job Overview

  • Job Title: Cloud Data Engineering Lead
  • Company: Qode
  • Location: Vietnam, Kitgum, Uganda
  • Job Type: Full-Time
  • Category: Data Engineering & Architecture
  • Date Posted: 2025-07-29

🚀 Role Summary

  • Lead the design and delivery of modern cloud-native data architectures, driving innovation and solving complex data challenges.
  • Manage and grow a high-performing team of data and AI engineers, shaping project outcomes and long-term team capability.
  • Collaborate with DevOps and cloud architects to deploy scalable solutions on AWS/GCP/Azure.
  • Take ownership of project delivery and technical direction, from scoping to execution.
  • Stay ahead of technology trends and recommend new approaches for data strategy and architecture.

📝 Enhancement Note: This role offers significant influence over the company's long-term data and AI strategy, with the potential to evolve into a Chief of Data position.

💻 Primary Responsibilities

  • Architect and Build: Design and implement modern cloud-native data platforms, including data lakes, lakehouses, and data warehouses.
  • Develop Pipelines: Create robust data pipelines and ETL/ELT processes using modern frameworks like Airflow, Glue, and DBT.
  • Manage Real-Time Data: Handle real-time data ingestion and streaming solutions with Kafka, Flink, or Apache Beam.
  • Lead and Grow Teams: Lead and grow a high-performing team of data and AI engineers, shaping both project outcomes and long-term team capability.
  • Consult and Advise: Actively communicate with customers to gather requirements, provide status updates, and ensure alignment. Consult and advise clients on data strategy, architecture design, and AI implementation.
  • Collaborate and Strategize: Work closely with CTO and CEO in strategic decisions, including technology roadmap and best practices.

📝 Enhancement Note: This role requires a strong balance of technical expertise, leadership skills, and client-facing communication to drive success in both project delivery and team management.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field. Relevant experience may substitute for formal education.

Experience: 6-7+ years of experience in data engineering, cloud data architecture, or related roles. Proven experience leading data projects, with a strong hands-on background in data engineering tasks.

Required Skills:

  • Proficiency in SQL and Python, with a disciplined coding mindset.
  • Strong experience with cloud services such as AWS (S3, Glue, Redshift, Lambda, Step Functions); GCP or Azure.
  • Familiarity with orchestration tools like Apache Airflow, Dagster, or Luigi.
  • Experience with modern data integration tools (Fivetran, Airbyte, Talend, or AWS Glue).
  • Solid background working with query engines and processing frameworks (Presto, Flink, PySpark, or DBT).
  • Understanding of modern databases and real-time data systems (Redshift, MongoDB, Kafka, and PostgreSQL).
  • Excellent communication skills, with comfort engaging in client-facing activities.

Preferred Skills:

  • Experience working with AI/ML projects or teams.
  • Exposure to Big Data platforms like Snowflake, Databricks, or Hadoop.
  • AWS Data Analytics or other relevant cloud certifications.
  • Strong leadership potential with the ability to influence teams and drive decisions.

📝 Enhancement Note: Candidates with experience in AI/ML projects and relevant cloud certifications will have an advantage in this role, as they can contribute to the company's AI strategy and growth.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

  • Demonstrate a diverse range of data engineering projects, showcasing your ability to design and implement modern cloud-native data platforms.
  • Highlight your experience with data pipelines, ETL/ELT processes, and real-time data ingestion using relevant tools and technologies.
  • Include examples of your leadership and team management skills, such as mentoring, project planning, and stakeholder communication.

Technical Documentation:

  • Showcase your ability to document data pipelines, ETL/ELT processes, and data transformations using clear, concise, and well-commented code.
  • Demonstrate your understanding of data governance, data quality, and metadata management practices.
  • Include any relevant case studies or success stories that highlight your ability to drive business value through data engineering.

📝 Enhancement Note: As this role involves both technical leadership and project delivery, your portfolio should emphasize your ability to manage teams, communicate effectively with stakeholders, and drive data projects to successful completion.

💵 Compensation & Benefits

Salary Range: The salary range for this role is estimated to be between 150,000,000 VND (Vietnamese Dong) and 250,000,000 VND (Vietnamese Dong) per year, based on industry standards for senior data engineering roles in Vietnam. This estimate takes into account the candidate's experience level, the company's size, and the regional cost of living.

Benefits:

  • 13-month salary
  • Project-based bonus
  • Access to certifications
  • Annual company trips
  • Fun team events
  • Full benefits package

Working Hours: The standard working hours for this role are 40 hours per week, with flexibility for project deadlines and maintenance windows.

📝 Enhancement Note: The salary range provided is an estimate based on regional market research and industry standards for senior data engineering roles. The actual salary may vary depending on the candidate's experience, skills, and the company's internal compensation structure.

🎯 Team & Company Context

🏢 Company Culture

Industry: Qode operates in the technology consulting and cloud services industry, empowering 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.

Company Size: Qode is a mid-sized company, with a dynamic team of engineers, architects, and DevOps experts passionate about delivering impactful cloud solutions across industries, including FinTech, BFSI, and Digital Startups. This size offers the advantage of a collaborative work environment with ample opportunities for growth and innovation.

Founded: Qode was founded with a mission to transform businesses through cloud technology, data, and AI. Their focus on client success and innovative problem-solving has driven their growth and success in the market.

Team Structure:

  • The data and AI practice is a growing team within Qode, focusing on designing and implementing modern data architectures, data pipelines, and AI solutions.
  • The team works closely with Qode's cloud architecture and DevOps teams to deliver integrated solutions that meet client needs.
  • The Cloud Data Engineering Lead will report directly to the CTO and work closely with the CEO in strategic decision-making.

Development Methodology:

  • Qode follows Agile development methodologies, with a focus on iterative development, continuous integration, and collaboration.
  • The data and AI practice uses modern data engineering tools and frameworks, such as Apache Airflow, DBT, and AWS Glue, to ensure efficient and scalable data processing.
  • The team emphasizes code quality, testing, and documentation to maintain high standards and facilitate knowledge sharing.

Company Website: Qode

📝 Enhancement Note: Qode's focus on client success and innovative problem-solving creates an environment where the Cloud Data Engineering Lead can make a significant impact on the company's growth and the success of its clients.

📈 Career & Growth Analysis

Web Technology Career Level: This role is a senior-level position, offering significant influence over the company's long-term data and AI strategy. The Cloud Data Engineering Lead will have the opportunity to grow into a Chief of Data position, taking on increased responsibility for the company's data and AI initiatives.

Reporting Structure: The Cloud Data Engineering Lead will report directly to the CTO and work closely with the CEO, providing strategic input on data and AI initiatives and driving the company's data practice forward.

Technical Impact: In this role, you will have a direct impact on the company's data and AI capabilities, driving innovation and ensuring that Qode's clients receive cutting-edge data solutions. Your work will enable Qode to expand its offerings in the data and AI space, positioning the company as a leader in cloud technology and data-driven transformation.

Growth Opportunities:

  • Team Expansion: As the Cloud Data Engineering Lead, you will have the opportunity to grow and expand the data and AI team, mentoring junior team members and driving the team's technical capabilities forward.
  • Technical Leadership: This role offers significant opportunities for technical leadership, with the potential to influence Qode's technology roadmap and best practices in data and AI.
  • Strategic Influence: Working closely with the CTO and CEO, you will have the opportunity to shape Qode's long-term data and AI strategy, driving the company's growth and success in the market.

📝 Enhancement Note: The Cloud Data Engineering Lead role at Qode offers significant opportunities for career growth and technical leadership, with the potential to make a lasting impact on the company's data and AI capabilities.

🌐 Work Environment

Office Type: Qode offers a modern, dynamic, and multicultural work environment, with a focus on collaboration and innovation. The company's offices are designed to facilitate teamwork and knowledge sharing, with ample space for both focused work and casual interaction.

Office Location(s): Qode's offices are located in Vietnam, with the potential for remote work arrangements for the right candidate.

Workspace Context:

  • Collaboration: Qode's offices are designed to encourage collaboration, with open-plan workspaces and dedicated team areas for brainstorming and problem-solving.
  • Technology: The company provides state-of-the-art technology for its employees, including multiple monitors, testing devices, and access to the latest development tools and frameworks.
  • Flexibility: Qode offers a flexible working environment, with the option to work remotely or from the office, depending on the employee's preferences and the project's needs.

Work Schedule: The standard working hours for this role are 40 hours per week, with flexibility for project deadlines and maintenance windows. Qode offers a flexible work arrangement, allowing employees to balance their work and personal lives effectively.

📝 Enhancement Note: Qode's work environment is designed to foster collaboration, innovation, and employee satisfaction, creating an ideal setting for the Cloud Data Engineering Lead to thrive and drive the company's data and AI initiatives forward.

📄 Application & Technical Interview Process

Interview Process:

  1. Technical Assessment (60 minutes): Demonstrate your technical skills through a hands-on assessment, focusing on data engineering tasks, such as designing data pipelines, ETL/ELT processes, and real-time data ingestion.
  2. System Design Discussion (60 minutes): Present your approach to designing and implementing modern cloud-native data platforms, showcasing your ability to work with complex data systems and make strategic decisions.
  3. Team Fit & Cultural Interview (30 minutes): Discuss your leadership style, team management approach, and cultural fit with Qode's dynamic and collaborative work environment.
  4. Final Evaluation (30 minutes): Review your technical skills, leadership potential, and alignment with Qode's data and AI strategy, providing feedback and next steps for the hiring process.

Portfolio Review Tips:

  • Case Study Structure: Present your data engineering projects using a clear and concise case study structure, highlighting your approach to data pipeline design, ETL/ELT processes, and real-time data ingestion.
  • Technical Deep Dive: Prepare to discuss the technical details of your projects, including the tools and frameworks used, the challenges faced, and the solutions implemented.
  • User Experience Focus: Emphasize the user experience aspects of your data engineering projects, demonstrating your ability to design data solutions that meet business needs and drive value for Qode's clients.

Technical Challenge Preparation:

  • Data Pipeline Design: Brush up on your data pipeline design skills, focusing on modern frameworks like Apache Airflow, DBT, and AWS Glue.
  • Real-Time Data Ingestion: Familiarize yourself with real-time data ingestion tools and technologies, such as Kafka, Flink, or Apache Beam.
  • Cloud Services: Review your knowledge of cloud services, with a focus on AWS, GCP, or Azure, to ensure you can design and implement scalable data solutions.

ATS Keywords: [Apache Airflow, AWS, Data Engineering, Data Pipeline, ETL, GCP, Kafka, Leadership, Python, Real-Time Data, SQL, Team Management, Cloud Services, Data Architecture]

📝 Enhancement Note: The interview process for the Cloud Data Engineering Lead role at Qode is designed to assess both your technical skills and your ability to lead and grow a high-performing team, ensuring a strong fit with the company's dynamic and collaborative work environment.

🛠 Technology Stack & Web Infrastructure

Data Platforms & Tools:

  • Cloud Services: AWS, GCP, or Azure
  • Data Warehousing: Amazon Redshift, Google BigQuery, or Azure Synapse Analytics
  • Data Lakes & Lakehouses: Amazon S3, Google Cloud Storage, or Azure Data Lake Storage Gen2
  • Data Integration: AWS Glue, Talend, or Fivetran
  • ETL/ELT Tools: AWS Glue, Talend, or Pentaho
  • Real-Time Data Ingestion: Apache Kafka, Flink, or Apache Beam
  • Data Processing: PySpark, Apache Spark, or AWS Lambda
  • Data Transformation: DBT, AWS Glue, or Talend
  • Data Visualization: Tableau, Power BI, or Looker

Programming Languages & Frameworks:

  • Programming Languages: Python, SQL
  • Data Processing Frameworks: PySpark, Apache Spark, or AWS Lambda
  • Data Transformation Frameworks: DBT, AWS Glue, or Talend
  • Orchestration Tools: Apache Airflow, AWS Step Functions, or Prefect

Database Systems:

  • Relational Databases: Amazon RDS, Google Cloud SQL, or Azure SQL Database
  • NoSQL Databases: Amazon DynamoDB, Google Cloud Firestore, or Azure Cosmos DB
  • Data Warehouse Databases: Amazon Redshift, Google BigQuery, or Azure Synapse Analytics

📝 Enhancement Note: Qode's technology stack is designed to provide a comprehensive and scalable data engineering environment, enabling the Cloud Data Engineering Lead to design and implement modern cloud-native data platforms that meet the needs of Qode's diverse client base.

👥 Team Culture & Values

Data Engineering Values:

  • Innovation: Qode values innovation in data engineering, encouraging its team members to explore new tools, frameworks, and approaches to drive business value through data.
  • Collaboration: Qode emphasizes collaboration between data engineers, data scientists, and business stakeholders, ensuring that data solutions meet business needs and drive user impact.
  • Quality: Qode is committed to maintaining high standards of data quality, code quality, and documentation, ensuring that data solutions are scalable, performant, and reliable.
  • User Focus: Qode prioritizes the user experience in data engineering, designing data solutions that meet business needs and drive value for Qode's clients.

Collaboration Style:

  • Cross-Functional Integration: Qode's data engineering team works closely with other teams, including cloud architecture, DevOps, and data science, to deliver integrated solutions that meet client needs.
  • Code Review Culture: Qode emphasizes code review and peer programming, ensuring that data solutions are well-documented, tested, and optimized for performance.
  • Knowledge Sharing: Qode fosters a culture of knowledge sharing, with regular team meetings, workshops, and training sessions to help team members develop their skills and stay up-to-date with the latest trends in data engineering.

📝 Enhancement Note: Qode's data engineering team values innovation, collaboration, quality, and user focus, creating an environment where the Cloud Data Engineering Lead can drive the company's data and AI initiatives forward and make a lasting impact on Qode's clients.

⚡ Challenges & Growth Opportunities

Technical Challenges:

  • Data Silos: Design and implement data integration solutions to break down data silos and enable seamless data flow across Qode's clients' organizations.
  • Data Governance: Establish and maintain robust data governance practices, ensuring data quality, security, and compliance with relevant regulations and standards.
  • Real-Time Data Processing: Develop and optimize real-time data processing pipelines, enabling Qode's clients to gain real-time insights and make data-driven decisions.
  • Scalability: Design and implement scalable data solutions that can handle Qode's clients' growing data needs, ensuring optimal performance and cost-efficiency.

Learning & Development Opportunities:

  • Emerging Technologies: Stay up-to-date with the latest trends in data engineering, exploring emerging technologies and tools to drive innovation and business value for Qode's clients.
  • Leadership Development: Develop your leadership skills through mentoring, coaching, and team management opportunities, driving the growth and success of Qode's data and AI practice.
  • Architecture Decision-Making: Gain experience in architecture decision-making, working closely with Qode's cloud architecture and DevOps teams to design and implement scalable and secure data solutions.

📝 Enhancement Note: The Cloud Data Engineering Lead role at Qode offers significant opportunities for technical growth and leadership development, with the potential to make a lasting impact on the company's data and AI capabilities and drive business value for Qode's clients.

💡 Interview Preparation

Technical Questions:

  • Data Pipeline Design: Describe your approach to designing and implementing data pipelines using modern frameworks like Apache Airflow, DBT, and AWS Glue. Discuss any challenges you've faced and how you overcame them.
  • Real-Time Data Ingestion: Explain your experience with real-time data ingestion tools and technologies, such as Kafka, Flink, or Apache Beam. Describe a real-time data ingestion project you've worked on and the outcomes achieved.
  • Cloud Services: Demonstrate your knowledge of cloud services, with a focus on AWS, GCP, or Azure. Discuss your experience designing and implementing scalable data solutions on these platforms.

Company & Culture Questions:

  • Data Strategy: Explain how you would approach developing a data strategy for Qode, considering the company's goals, client needs, and emerging data trends.
  • Team Management: Describe your approach to team management, focusing on mentoring, coaching, and driving team success. Discuss any challenges you've faced and how you've overcome them.
  • Client Communication: Explain how you would communicate with Qode's clients to gather requirements, provide status updates, and ensure alignment with their data needs and business goals.

Portfolio Presentation Strategy:

  • Case Study Structure: Present your data engineering projects using a clear and concise case study structure, highlighting your approach to data pipeline design, ETL/ELT processes, and real-time data ingestion.
  • Technical Deep Dive: Prepare to discuss the technical details of your projects, including the tools and frameworks used, the challenges faced, and the solutions implemented.
  • User Experience Focus: Emphasize the user experience aspects of your data engineering projects, demonstrating your ability to design data solutions that meet business needs and drive value for Qode's clients.

📝 Enhancement Note: The interview process for the Cloud Data Engineering Lead role at Qode is designed to assess both your technical skills and your ability to lead and grow a high-performing team, ensuring a strong fit with the company's dynamic and collaborative work environment.

📌 Application Steps

To apply for this Cloud Data Engineering Lead position at Qode:

  1. Submit Your Application: Click on the application link provided and submit your application through the Workable platform.
  2. Prepare Your Portfolio: Tailor your portfolio to showcase your data engineering projects, focusing on data pipeline design, ETL/ELT processes, and real-time data ingestion. Include case studies that demonstrate your ability to work with complex data systems and make strategic decisions.
  3. Optimize Your Resume: Highlight your technical skills, leadership experience, and relevant industry certifications. Include specific examples of your data engineering projects and the impact you've made on business outcomes.
  4. Research Qode: Familiarize yourself with Qode's company culture, values, and technology stack. Prepare for your interviews by understanding Qode's data and AI strategy, as well as the company's goals and client needs.
  5. Prepare for Technical Challenges: Brush up on your data pipeline design skills, real-time data ingestion tools, and cloud services. Review your knowledge of Qode's technology stack and be ready to discuss your approach to data engineering challenges.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web technology industry-standard assumptions. All details should be verified directly with Qode before making application decisions.

Application Requirements

Candidates must have 6-7+ years of experience in data engineering or related roles, with strong proficiency in SQL and Python. Excellent communication skills and past experience leading data projects are essential.