PRINCIPAL DATA ENGINEER

Job Description

WHAT YOU’LL DO – KEY RESPONSIBILITIES

• Customer liaison & discovery
  • Lead discovery sessions with technical and non-technical stakeholders to understand source systems, data lineage, business definitions, and reporting needs.
  • Map business KPIs/metrics to available data and identify gaps or remediation required.
• Data modeling & metric engineering
  • Design logical and physical data models (facts, dimensions, hierarchies, slowly changing dimensions) that reflect customer business semantics and support the AI Data Analyst’s metric definitions.
  • Define canonical metric specifications (metric definition, calculation SQL/DSL, cohort logic, edge cases).
• Platform integration
  • Implement data connections, ingestion pipelines, and schema mappings into the SaaS platform (or customer’s cloud data layer) ensuring freshness, reliability, and observability.
  • Configure dimensions, attributes, and metric metadata inside the platform so the AI models can consume and reason about the data.

• Validation & QA
  • Develop and execute test plans to validate AI Data Analyst outputs against agreed-upon metric specs and ground-truth reports; quantify accuracy and identify root causes for discrepancies.
  • Create automated and manual validation suites (unit tests, reconciliation queries, data quality checks).
• Project & stakeholder management
  • Create project plans, manage timelines, set realistic expectations, and communicate status/risks to customers and internal stakeholders.
  • Facilitate sign-offs on metric definitions, data readiness, and production cutovers.
• Risk, security & governance
  • Identify data and model risks (PII exposures, inference errors, stale data) and put mitigation controls in place.
  • Ensure implementations comply with customer security, data governance, and regulatory requirements.
• Knowledge transfer & documentation
  • Produce clear runbooks, metric spec docs, and onboarding artifacts. Train customer users and internal support teams for ongoing operations.
• Continuous improvement
  • Feed product/engineering with requirements and lessons learned to improve platform data modeling capabilities and onboarding playbooks.

MUST-HAVE QUALIFICATIONS

• 5+ years experience in data engineering or analytics engineering, with a strong focus on data modeling for enterprises (experience with Fintech industries strongly preferred).
• Proven track record of translating business metric requirements into production-ready data models (fact/dimension modeling, SCD handling, hierarchies).
• Excellent stakeholder management with experience gathering requirements from both technical teams (ETL/analytics, data platform) and non-technical business teams (finance, product, ops).
• Strong SQL skills — able to author, optimize, and review complex analytic queries end-to-end.
• Experience validating analytical outputs and building reconciliation/QA processes.
• Demonstrable project management and expectation-management skills for customer engagements.
• Familiarity with data risk and governance concerns (PII handling, access controls, auditability).
• Excellent written and verbal communication skills; able to produce clear metric specs and runbooks.

Highly desirable (nice-to-have)

• Hands-on experience with modern cloud data stacks — AWS (S3, Glue, Redshift, Lambda), Databricks, or Snowflake.
• Experience building or architecting data lakes, Delta Lake, and streaming/batch pipelines.
• Familiarity with orchestration tools (Airflow, Prefect) and analytics engineering tools (dbt).
• Experience with Spark, Python (pandas/pySpark), and event streaming (Kafka).
• Experience working directly with enterprise security/compliance teams and implementing data access controls.
• Prior experience in a customer-facing or consulting/onboarding role for an analytics or ML product.
• Understanding of model evaluation and basic ML/LLM validation techniques (for AI output verification).

CORE COMPETENCIES & SOFT SKILLS

• Customer-first mentality: patient, thorough, and able to build trust with enterprise stakeholders.
• Structured problem solving: break ambiguous business needs into measurable metric specs and test cases.
• Project management: scope, plan, manage trade-offs, and deliver with clear milestones.
• Risk & expectation management: proactively surface issues and propose mitigations.
• Collaboration: work closely with product, platform engineering, data science, and customer success.

BENEFITS

• Full-time insurance follow VN Labor laws
• Work equipment support.
• Annual bonus, performance-based bonus.
• Monthly compensation package to help you work and live better, remotely!
• Fully support for exams to get Certification and skills improvement training.
• Receive 22 paid leaves on your 5th years. We encourage you to take one month off work.
• A competitive salary and benefits package

WORKING TYPE: Remote full-time

DOMAIN: Fintech (Sing client)

WORKING TIME: From Monday to Friday ( 8 hours/ day)

SHUI: 30%

SALARY RANGE: Max 70M gross

THE HIRING PROCESS

• R1: Assessment (optional)
• R2: Live code + technical interview (VN team)
• R3: Meet client (1-2 rounds)

Please note that all rounds will be conducted in English.