Enterprise Database Architect (Fintech)

Top 3 reasons to join us

• International fintech & digital banking firm
• Top salary, bonus & stock options
• Work & travel globally with innovation teams

Job description

Role Summary

Vietpay is hiring an Enterprise Database Architect who owns the design, modeling, optimization, and governance of Vietpay's core data systems. This role is hands-on and architecture-led. You will build and evolve relational, document, and graph data models, ensure high performance for transaction-heavy fintech workloads, and define standards that help engineering teams ship reliable microservices with clean data contracts. You will also lead the modernisation of legacy data systems, own zero-downtime production migrations, and establish the data foundations that support compliance, analytics, and AI-enabled workflows. Strong experience in SQL, MongoDB, Neo4j, and observability using Grafana is required.

Key Responsibilities

1) Database Architecture and Data Modeling

• Own end-to-end data architecture for Vietpay's core platforms: acquiring, settlement, reconciliation, lending, risk, and reporting.
• Design and document relational schemas, MongoDB document models, and Neo4j graph models aligned to business domains.
• Define modeling standards, naming conventions, schema versioning, and change management practices.
• Design data integrity patterns for fintech: double-entry ledgers, audit trails, idempotency keys, settlement-friendly structures, and chargeback-ready schemas.
• Partner with product and engineering to translate business workflows into clean entities, relationships, and scalable storage patterns.

2) Performance, Scalability, and Optimization

• Lead performance tuning across query design, indexes, partitioning, caching, and storage strategy.
• Optimize for high-throughput workloads with strict correctness requirements: transaction posting, settlement, and reporting.
• Define scaling approaches such as read replicas, sharding strategies, and workload separation where appropriate.
• Review and improve slow queries and critical paths, establish performance baselines and regression checks.
• Guide teams on efficient data access patterns and cost-aware design to reduce infrastructure spend.

3) Microservices, Data Contracts, and API-Driven Data Systems

• Architect database patterns that integrate cleanly with microservices and API layers, favouring service-owned data boundaries.
• Define and enforce data contracts between microservices teams: schema ownership, versioning agreements, and integration guidelines that reduce coupling and prevent silent failures.
• Define data consistency approaches (strong vs eventual), including event-driven propagation, outbox patterns, and retries.
• Design strategies for deduplication, concurrency control, and safe retries across distributed services.
• Support schema evolution across services without breaking production, including backward-compatible migrations and deprecation policies.

4) Observability and Operations (Grafana)

• Establish database observability standards: metrics, alerts, SLOs, and dashboards covering latency, throughput, error rates, replication lag, capacity, and resource saturation.
• Build and maintain Grafana dashboards tailored to fintech workloads: transaction throughput, settlement lag, reconciliation drift, and lock contention.
• Partner with DevOps and SRE on backups, restore testing, failover, and disaster recovery runbooks.
• Support incident response and post-mortems, drive remediation to prevent repeat database-related issues.

5) Legacy Modernisation and Production Data Migration

• Assess and document legacy data models, tracing undocumented schemas and data flows before making any changes.
• Migrate tightly coupled monolith databases to service-owned schemas without disrupting live production traffic.
• Apply expand-contract patterns, dual-write strategies, and feature flags to execute multi-phase migrations incrementally rather than via big-bang cutovers.
• Write migration scripts and data backfill jobs that are idempotent, auditable, and reversible, tested in staging against production-scale data volumes.
• Manage zero-downtime schema migrations on live fintech tables, including index builds, column additions, and constraint changes under real production load.
• Maintain backward compatibility across API and schema versions throughout migration windows.
• Actively manage technical debt in the data layer: track it, prioritise it against product work, and communicate trade-offs clearly to engineering and product stakeholders.
• Deliver modernisation projects with zero critical incidents and a documented rollback plan for every change.

6) Security and Governance

• Implement security best practices: least privilege access, encryption at rest and in transit, audit logging, and secure secrets handling.
• Define governance for PII and sensitive financial data: retention policies, masking and tokenisation strategies, and periodic access reviews.
• Ensure all data architectures support compliance and auditability requirements for financial services, including reconciliation trails and regulatory reporting readiness.
• Maintain data lineage documentation so compliance and audit teams can trace the origin and transformation of any financial record.

7) AI and Analytics Readiness

• Structure datasets, schemas, and metadata so that analytics and AI teams can discover patterns and build models without requiring custom data extracts.
• Apply AI tools practically in day-to-day database work: query plan analysis, automated anomaly detection on metrics, schema documentation generation, and pattern discovery on historical transaction data.
• Design data foundations that support ETL and ELT pipelines, with clean separation between transactional and analytical workloads.
• Stay current with AI-enabled database tooling and proactively propose where it can improve performance, governance, or observability.

8) Engineering Leadership and Standards

• Define and enforce database engineering standards across all teams: modeling conventions, migration practices, observability requirements, and review checklists.
• Conduct data model and query reviews as part of the engineering delivery process.
• Mentor engineers on data design, performance reasoning, and production-safe change management.
• Produce and maintain architecture decision records, schema documentation, and operational runbooks that remain useful as the team scales.

Your skills and experience

Job Requirements

• Must be fluent in English (spoken and written).
• 7+ years of experience in enterprise database engineering or architecture roles.
• Strong hands-on expertise in SQL, including data modeling, transactions, indexing, query optimisation, and schema design for financial workloads.
• Strong hands-on expertise in MongoDB, including document modeling, indexing, performance tuning, and familiarity with replication and sharding.
• Hands-on experience with Neo4j, including graph data modeling and Cypher query design, with ability to identify the right graph use cases.
• Proven experience with fintech-specific data patterns: double-entry ledger design, idempotency key storage, settlement reconciliation schemas, audit log structures, and chargeback-ready data models.
• Demonstrated experience delivering zero-downtime schema migrations on production databases under real load, using expand-contract or equivalent patterns.
• Proven experience modernising or refactoring legacy database systems, with a track record of successful delivery without production incidents.
• Proven experience supporting microservices platforms, including defining data contracts between services and managing schema evolution safely.
• Strong operational mindset: monitoring, backups, restore testing, high availability, and disaster recovery planning.
• Practical experience with observability and dashboards using Grafana and related metrics and alerting stacks.
• Experience in financial services, banking, insurance, or other regulated environments is required.

Preferred Qualifications

• Experience with event-driven architectures and messaging systems such as Kafka, RabbitMQ, or equivalent, including outbox pattern implementation.
• Experience with analytics and warehousing patterns: ETL and ELT pipelines, dimensional modeling, or data lake concepts.
• Exposure to AI and ML data preparation workflows, or practical use of AI tools for query optimisation, schema review, and anomaly detection.
• Experience with data lineage and catalog tooling.
• Familiarity with NAPAS, Visa, or Mastercard payment workflows and their data implications.
• Experience with migration and versioning tools such as Flyway or Liquibase.

Tools and Working Methods

• Databases: PostgreSQL or MySQL, MongoDB, Neo4j; strong SQL-first discipline for transactional data.
• Observability: Grafana dashboards, plus common metric and alerting stacks as used by DevOps and SRE teams.
• Data operations: migration and versioning tools (Flyway, Liquibase, or equivalent), backup and restore automation, performance profiling tools.
• Collaboration: Jira, Confluence or Notion, Git-based workflows, and clear written documentation including architecture decision records and schema change logs.

Why you'll love working here

• International fintech & digital banking firm
• Top salary, bonus & stock options
• Work & travel globally with innovation teams
• Social insurance based on full salary 
• Full Training will be provided to Candidate