Data Analyst

Senior Data Engineer

Ho Chi Minh - Viet Nam

Product

Designing and implementing scalable data architectures aligned with business objectives Building and maintaining data pipelines for ingestion, transformation, and delivery using modern orchestration tools Architecting data solutions across data warehousing, data lakes, and real-time analytics Creating and maintaining data models and documenting the enterprise data landscape Building and maintaining data infrastructure supporting ML model training, deployment, and monitoring Designing and implementing vector database solutions for AI-powered features Developing data pipelines for AI-driven capabilities and ensuring global scalability Implementing DataOps practices to ensure data quality, lineage, and governance Defining and enforcing data strategy and architectural principles Building monitoring and alerting for pipeline health, data quality, and SLA compliance Optimizing query performance and cost efficiency Collaborating with product and engineering teams to translate business requirements into data solutions Driving adoption of modern data practices across the organization Contributing to architectural reviews and technical decision-making Taking ownership of data problems through to resolution

Negotiation

Data Analyst

Ho Chi Minh - Viet Nam

Offshore

• Data Analyst

Gather, sanitize, and evaluate data from diverse origins to meet business requirements. Construct and maintain fundamental analytical datasets and reports for different teams. Validate and verify data to uphold accuracy, consistency, and dependability. Apply statistical and analytical methods to identify patterns and potential opportunities. Work closely with business stakeholders to grasp needs and provide actionable insights. Carry out thorough analyses of customer behavior, product usage, and market trends. Translate intricate data findings into clear, influential recommendations for business strategies. Support data-informed decision-making across various teams including marketing, operations, finance, and product teams. Create dashboards and Key Performance Indicator (KPI) tracking tools for monitoring business performance. Devise and conduct experiments (e.g., A/B tests) to evaluate initiatives. Deliver insights in a clear and convincing manner to both technical and non-technical audiences. Encourage data literacy by aiding colleagues in understanding and utilizing analytical tools. Share best practices in data analysis, visualization, and reporting. Contribute to documentation and training to elevate organizational analytics proficiency. Allocate time to cross-team projects aimed at enhancing company-wide data capabilities.

Negotiation

Engineering Manager (Data Platform)

Ho Chi Minh - Viet Nam

Offshore

• Data Engineering
• Management

Agile Team Leadership: Guide and coach Agile teams to uphold engineering standards, manage sprint backlogs, clarify responsibilities, ensure code quality, enforce development guardrails, and drive rigorous testing practices. Agile Data Delivery: Oversee Agile execution across data platforms, maintaining excellence in data quality, testing, code review practices, CI/CD pipelines, documentation, and operational readiness. Cross-Functional Collaboration: Partner with data architects, product managers, analytics teams, platform engineers, and governance stakeholders to deliver data capabilities aligned with business priorities. Roadmap Ownership: Lead the execution of the data engineering roadmap, balancing immediate delivery needs with long-term platform sustainability. Architecture & Design: Contribute to the design of data platform architecture across ingestion, transformation, storage, and consumption layers. Engineer Development: Coach engineers to become T-shaped professionals, capable of working across batch processing, streaming, analytics engineering, and platform operations. Technical Debt Remediation: Own and prioritize the resolution of technical and data debt, including legacy pipelines, performance bottlenecks, and data quality issues. Modern Practices: Stay current with evolving data engineering tools, methodologies, and patterns-particularly within the Databricks ecosystem. Lifecycle Accountability: Ensure end-to-end ownership of data solutions, from design and build through deployment, monitoring, and ongoing support. Team Empowerment: Foster self-sufficient, disciplined teams accountable for the reliability and resilience of data products. Process Excellence: Lead initiatives to enhance data delivery through automation, observability, and operational best practices. Continuous Improvement: Inspire teams to innovate, experiment, and embrace continuous delivery as part of their culture. Career Growth: Drive career development for data engineers, partnering with HR to manage performance and define growth pathways.

Negotiation