Lead Data Engineer (Snowflake | AWS | Azure | GCP)
Hands-on technical lead role at iZeno (Logicalis APAC) in Kuala Lumpur: design and deliver enterprise cloud data platforms — warehousing, data lakes, big data — on Snowflake and AWS/Azure/GCP, while mentoring data engineers and driving client-facing project delivery. Core stack: SQL, Python, and data modelling (Kimball, Data Vault).
- Compensation
- Not disclosed
- Location
- Kuala Lumpur, Malaysia
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor's Degree or Diploma in Computer Science, Information Technology, or related discipline
- Minimum 7-8 years of experience in Data Engineering, Data Warehousing, Data Lakes, Big Data, or Analytics, including leading technical teams or projects
- Strong expertise in Data Modelling (Kimball, Star Schema, Snowflake Schema, Data Vault, 3NF)
- Hands-on experience designing and delivering enterprise-scale data solutions, with strong understanding of ETL/ELT and modern data architecture
- Expert proficiency in SQL and Python
- Experience with Snowflake or modern cloud data platforms on AWS, Azure, or GCP
- Familiarity with Linux/Unix environments
Nice to have
- Experience with Java or Scala
- Exposure to Docker and Kubernetes
- Knowledge of Data Governance, Data Quality, or Master Data Management (MDM)
Full description
Founded in 2003, iZeno was built on one conviction: enterprises deserve technology that doesn't just keep pace with change — it drives it.
As part of Logicalis Asia Pacific — the Architects of Change — our team of 180+ in-house innovators has delivered over 500+ enterprise solutions across Singapore, Malaysia, Indonesia, Thailand, and the Philippines, spanning AI & machine learning, cloud, DevOps, data analytics, CRM & CX, and ITSM.
At iZeno, you'll belong to a high-performing team where collaboration is real and ideas are welcomed. You'll grow through mission-critical projects that challenge you from day one, backed by the global resources and partner ecosystem of Logicalis. And you'll thrive in an environment that invests in your development, your wellbeing, and your career.
We are proud to be a Great Place to Work Certified organisation — because we believe the same dedication we bring to our clients belongs to our people too.
No challenge is too complex. No ambition too large.
About the Role
As a Lead Data Engineer, you will play a key role in designing and delivering modern enterprise data platforms while providing technical leadership to both project teams and clients. This is a hands‑on technical leadership position where you'll shape data architecture, mentor engineers, and work closely with stakeholders to deliver scalable, cloud-based data solutions.
Key Responsibilities
- Lead the architecture, design, and implementation of enterprise solutions involving Data Analytics, Data Warehousing, Data Lakes, and Big Data technologies.
- Design, review, and govern Conceptual, Logical, and Physical Data Models to ensure scalability, data integrity, maintainability, and optimal performance.
- Provide technical leadership by mentoring, coaching, and guiding Data Engineers while ensuring high standards of code quality and engineering best practices.
- Collaborate closely with clients, business stakeholders, and solution architects to translate business requirements into technical solutions and strategic implementation roadmaps.
- Drive project delivery by overseeing technical execution, managing resources, timelines, risks, and deliverables throughout the Software Development Lifecycle (SDLC).
- Establish and promote best practices in data governance, data modelling, coding standards, and solution architecture across projects.
- Act as the primary technical escalation point for complex solution design and implementation challenges while effectively managing client expectations.
- Lead research, innovation, and technical upskilling initiatives to ensure the engineering team stays current with emerging technologies, modern cloud platforms, and evolving data engineering practices.