Tuyển dụng Senior/Lead - Data Engineering tại Hồ Chí Minh

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We are looking for a hands-on Lead Data Engineer to own the standardization of MoMo's core business data, with payment as the primary focus and marketing, promotion, and user behavior as the domains that follow, and to define the engineering standard that every data team at MoMo builds on.

This role sits between the Data Platform team and the domain data teams. On one side, you re-model fragmented, business-logic-heavy pipelines into a clean, layered warehouse that analysts and downstream systems can trust. On the other side, you turn the practices you prove in those domains into the company-wide standard: how a dbt project is structured, how models are tested and reviewed, how changes reach production through CI/CD, and how workloads run on the open Lakehouse stack (Spark, Iceberg, StarRocks).

You will work across a platform in transition: BigQuery today, an Iceberg-based Lakehouse (GCP and on-premise) tomorrow, with data privacy and PII boundaries as first-class design constraints rather than an afterthought.

Mô tả công việc

Data standardization across core business domains

Payment is the primary focus, followed by marketing, promotion, and user behavior.

• Audit the existing data assets in each domain, including pipelines, dbt models, ad-hoc tables, and reporting logic, and produce a consolidated map of sources, grain, ownership, duplication, and actual consumption.
• Design and deliver the target data model for each domain on a layered warehouse architecture, from raw ingestion through conformed dimensions and facts to the data marts that serve reporting, BI, and analytical products.
• Define and enforce conformed dimensions, surrogate keys, naming conventions, grain contracts, and slowly-changing-dimension handling shared across domains, so the same entity means the same thing in every mart. User, merchant, campaign, and time dimensions must be modeled once and reused, not rebuilt per domain.
• Model high-volume event and behavioral data (app events, campaign exposure, promotion issuance and redemption) into queryable, cost-efficient structures, including sessionization, funnel and journey modeling, and attribution-ready facts.
• Lead the migration of legacy pipelines into the new model with a clear cutover plan covering parallel run, reconciliation against legacy outputs, backfill strategy, and consumer migration, without breaking existing dashboards and reports.
• Partner with domain analysts and business owners to translate reporting requirements into stable data marts, and to retire redundant tables and one-off pipelines once the marts are trusted.
• Design PII handling into the model: pseudonymization boundaries, separation of sensitive and non-sensitive models, and placement of workloads according to data residency and privacy requirements.

Data development standards for MoMo

• Define the dbt standard for MoMo: project and folder structure, layering rules, model and column naming, materialization strategy, incremental patterns, macros and packages, sources and freshness, exposures, documentation, and the testing baseline (schema tests, data quality tests, contracts).
• Build the CI/CD pipeline for data: automated linting (SQLFluff or equivalent), compilation, slim/state-based CI on modified models, test execution on pull requests, environment promotion (dev to staging to prod), and release and rollback procedures.
• Establish the orchestration standard on Airflow: DAG structure and ownership, dependency and scheduling conventions, retry and alerting policy, SLA definition, backfill procedures, and runbooks for on-call.
• Own data quality and observability standards: freshness and volume monitoring, anomaly detection, lineage, incident response, and clear ownership and escalation for every data product.
• Produce the templates, reference examples, and documentation behind each standard, and run enablement sessions and code reviews so other data teams can adopt them independently.
• Influence and align data engineers across teams on these standards, and drive adoption through to completion rather than stopping at a published document.

Your Skills and Experience

• 5+ years of experience in Data Engineering, with proven hands-on delivery of production data warehouses or Lakehouse platforms at meaningful scale.
• Strong data modeling expertise, covering dimensional modeling (star schema, conformed dimensions, SCD), medallion / layered architecture, and the judgment to choose grain and structure that survive changing business requirements.
• Deep hands-on experience with dbt in production: project structure, incremental models, macros, tests, contracts, snapshots, and dbt performance tuning at scale (hundreds of models).
• Strong SQL and hands-on experience with a modern cloud warehouse, BigQuery preferred, including cost and performance optimization.
• Hands-on experience with Apache Spark and the open Lakehouse stack: Apache Iceberg (or Delta/Hudi), and an MPP/OLAP serving engine such as StarRocks, ClickHouse, or Trino.
• Hands-on experience with Apache Airflow (or equivalent orchestration) in production, including SLA ownership, backfills, and incident handling.
• Experience modeling high-volume event and behavioral data for analytics, including sessionization, funnel or journey analysis, and campaign or promotion attribution.
• Solid software engineering practices applied to data: Git workflow, code review, CI/CD (GitLab CI, GitHub Actions, or equivalent), testing, and Python for tooling and automation.
• Experience defining and rolling out engineering standards across multiple teams, with the ability to write clear designs, templates, and documentation, and to bring engineers along through review and enablement.
• Experience working with payment or financial