PRINCIPAL DATA ENGINEER
(Fintech project)
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% on full salary
SALARY RANGE: Up to 70M gross
Company: itworks.asia's Client (Outsourcing company)
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.
Contact: 0777-047-369 | www.itworks.asia
