Data Architect (AWS, Snowflake)

Introduction

DigiEx Group is a global technology partner specializing in Innovation Software Development, AI-powered solutions, Tech Talent services, and Digital Transformation. Headquartered in Vietnam, DigiEx helps startups and enterprises worldwide build scalable digital products and high-performing engineering teams.

At DigiEx, we embrace an AI-first engineering culture, empowering every team member to leverage AI technologies to build smarter software, improve productivity, and continuously innovate.

Key Responsibilities

• Lead the data architecture and discovery phase, including platform architecture, technology evaluation, and assessment of clients’ existing data environments.
• Establish the technical specification and architecture baseline, ensuring architecture decisions and delivery requirements are aligned across the engagement.
• Design and standardize data modeling approaches such as Kimball, Data Vault, or hybrid models based on business and technical requirements.
• Document key architecture decisions through Architecture Decision Records (ADRs), including alternatives considered, trade-offs, and rationale.
• Define data platform standards for security, governance, compliance, access control, data lineage, PII protection, and auditability.
• Provide technical leadership and architectural guidance to Senior Data Engineers, supporting complex technical decisions across projects.
• Proactively assess and mitigate data architecture risks, including scalability, integration, security, compliance, performance, and observability.
• Establish and maintain data engineering standards and hiring criteria, contributing to the growth and capability development of the data engineering practice.

Requirements

Must Have

• Education: Bachelor’s degree or above in Computer Science, Information Technology, Data Engineering, Data Architecture, or a related field. Master’s degree or equivalent advanced expertise is preferred.
• Experience: 7+ years of experience in Data Engineering or Data Architecture, including 3+ years in a Senior, Lead, or Architect capacity with ownership of platform-level technical decisions.
• Hands-on Engineering: Strong hands-on data engineering capability, with the ability to contribute at Senior Data Engineer level when required—not limited to high-level architecture and design.
• Data Architecture Assessment: Proven experience assessing existing client data systems and producing an actionable technical baseline, architecture roadmap, or modernization plan.
• Data Lakehouse Architecture: Experience with Delta Lake, Apache Iceberg, or Apache Hudi, with an understanding of architecture trade-offs and design decisions.
• AWS & Snowflake Expertise: Strong hands-on experience designing, implementing, and optimizing AWS data platforms and Snowflake, including architecture, performance tuning, scalability, and cost optimization.
• Infrastructure as Code: Experience with Terraform, Pulumi, or equivalent IaC tools, including the ability to design and review infrastructure architecture.
• Data Governance & Security: Experience with data lineage, access control, PII classification, data security, and compliance-aware architecture.
• Orchestration & Transformation: Experience with Airflow, dbt, or equivalent, with the ability to evaluate and recommend appropriate solutions.
• AI-Assisted Engineering: Comfortable reviewing and steering AI-generated code at scale, with the ability to identify correctness, security, performance, and maintainability issues efficiently.
• Regulated Domain: Experience working with at least one regulated data domain, such as Healthcare (HIPAA, HL7, FHIR), Pharmaceutical (21 CFR Part 11), Financial Services, or an equivalent regulated industry.
• Client Communication: Business-level English, with confidence to lead architecture discussions with CTOs, technical leaders, and client stakeholders.

Nice to Have

• Multi-cloud / Hybrid Cloud: Experience working across AWS, Snowflake, Azure, and/or GCP within the same engagement.
• ML/AI Data Infrastructure: Experience with feature stores, model registries, data versioning, and data architectures supporting ML/AI workloads at scale.
• End-to-End Ownership: Experience independently owning both data architecture design and delivery execution within the same engagement.
• Modern Data Stack: Experience with Kafka, Spark/PySpark, Databricks, or other modern data platform technologies.
• Architecture Leadership: Experience defining architecture standards, technology selection, technical roadmaps, and Architecture Decision Records (ADRs).