This role focused on data engineering teams with data warehousing, streaming and batch patterns, CI/CD for data pipelines,
Drive and coach Agile teams to deliver on engineering standards, sprint backlogs and plans, engineers’ responsibilities and performance management, code quality, adherence to development guardrails, and testing;
Drive Agile delivery across data platforms, ensuring high standards for; Data quality and testing, Code quality and review practices, CI/CD for data pipelines, Documentation and operational readiness
Collaborate closely with data architects, product managers, analytics teams, platform teams, and governance stakeholders to deliver data capabilities aligned with business priorities
Own the execution of the data engineering roadmap, balancing short-term delivery with long-term platform sustainability
Contribute to data platform architecture and design, including ingestion, transformation, storage, and consumption layers
Coach engineers to be T-shaped, capable of working across batch, streaming, analytics engineering, and platform concerns
Own and prioritise the remediation of technical and data debt, including legacy pipelines, performance issues, and data quality gaps
Stay current with modern data engineering tools, patterns, and methodologies, particularly within the Databricks ecosystem
Be accountable for the full lifecycle of data solutions, from design through build, deployment, monitoring, and support
Empower teams to be self-sufficient, disciplined, and accountable for the reliability of data products
Lead initiatives to improve data delivery processes, including automation, observability, and operational excellence
Motivate teams to continuously improve through innovation, experimentation, and continuous delivery
Drive career development and progression for data engineers, partnering with HR on performance management and growth paths
Requirements
Experience with one of the following/similar Technologies:
Strong experience with Databricks, Apache Spark, and lakehouse patterns
Deep understanding of data warehousing concepts, dimensional modelling, and analytics use cases
Experience building and operating batch and streaming data pipelines
Familiarity with Delta Lake, data versioning, and schema evolution
Understanding of data quality, data validation, lineage, and observability practices
Understanding about AWS (Lambda, S3, API Gateway, CLI, ECS, EKS…) or Google cloud platform is a must;
Formal Development methodologies;
Experience Required:
10+ years’ experience in software or data engineering
4+ years’ experience leading engineering teams, ideally in data, analytics, or platform domains
Proven ability to design and deliver end-to-end data platforms or major data initiatives
Experience working in regulated environments (banking or financial services preferred)
Strong people leadership skills, with a proven ability to mentor and grow data engineers
Experience producing and maintaining technical documentation, including architecture diagrams, runbooks, and data specifications
Proven ability to work with multiple stakeholders across geographies and manage competing priorities
Strong understanding of quality, reliability, and operational excellence in production data systems
Ownership of SLAs / SLOs for data availability and freshness
Collaboration with risk, compliance, and audit teams
Responsibility for data cost management and optimisation
Benefits
Meal and parking allowances
Full benefits and salary rank during probation.
Insurances such as Vietnamese labor law and premium health care for employees & family members
Values-driven, international working environment, and agile culture.
Overseas travel opportunities for training and work-related.
Internal Hackathons and company events (team building, coffee run, etc.).
Pro-Rate and performance bonus.
15-day annual + 3-day sick leave per year from the company.
