Engineering Manager - Data Platform

Description

• 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.
• Work-life balance 40-hr per week from Mon to Fri.