Data Ops - Senior Data Engineer
- Compensation
- Not disclosed
- Location
- Hồ Chí Minh, Vietnam
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 4+ years of experience in Data Engineering, Data Platform Engineering, or SRE, operating production data systems that other teams depend on;
- Strong SQL and hands-on experience with Apache Spark. You can read an unfamiliar job, determine why it is slow or failing, and fix it;
- Production experience with Airflow or a similar orchestrator, including incident handling, backfills, and SLA ownership; Hands-on Kubernetes and Linux troubleshooting, including logs, networking, reso
- Experience with a monitoring stack such as Prometheus and Grafana, including building useful dashboards and actionable alerts;
- Practical SRE experience: SLOs, on-call, incident response, root-cause analysis, and postmortems;
- Working knowledge of infrastructure as code and CI/CD using tools such as Terraform/Pulumi, Helm, or GitLab CI;
- Clear judgment during incidents and clear writing afterward. Runbooks and postmortems are part of the role's output;
- The ability to turn production evidence into a design change that other teams can adopt, and give a direct answer to users waiting on the platform;
Nice to have
- Experience with BigQuery or open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi;
- Experience with dbt and the lifecycle of building, testing, and releasing data models;
- Experience operating Kafka in production; Experience building internal platform or developer tooling used by other teams;
- Experience operating hybrid cloud and on-premise infrastructure.
Full description
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The Job
MoMo's data platform processes petabytes of data through thousands of pipelines. It powers product analytics, business performance deep dives, insight validation, and machine learning models for fraud detection, credit scoring, and personalization. Its reliability directly affects business decisions and product performance;
We are moving from a platform built primarily on BigQuery toward a hybrid lakehouse across cloud and on-premise infrastructure. The shift addresses growing demands for stronger data control, lower cost, and regulatory compliance. It also introduces harder operational problems: distributed compute, constrained capacity, platform migration, and the separation of workloads by data sensitivity;
We are looking for a Data Engineer who can keep the current platform dependable while helping build the next one. You will troubleshoot failures across the stack, strengthen reliability and observability, automate recurring operations, and migrate production workloads into the new architecture.
Job Description
- Operate the data platform. Maintain the reliability, availability, performance, and scalability of the lakehouse and its supporting infrastructure across cloud and on-premise environments;
- Keep core pipelines running. Monitor Airflow workloads, resolve failures and data delays, manage backfills, and identify resource bottlenecks before they affect downstream users;
- Improve reliability and observability. Define service-level objectives and build the monitoring, alerts, dashboards, runbooks, incident processes, and capacity plans needed to meet them;
- Resolve complex production incidents. Diagnose failures across orchestration, distributed compute, storage, networking, Kubernetes, and data pipelines. Lead recovery, root-cause analysis, and the changes that prevent recurrence;
- Improve performance and cost. Analyze compute, storage, and data-access patterns. Fix inefficient workloads and make deliberate trade-offs across cloud and on-premise capacity;
- Build compliant data boundaries. Redesign and migrate pipelines so PII and PII-free workloads run in separate environments with appropriate access controls and operational safeguards;
- Automate platform operations. Build tools for deployment, monitoring, recovery, validation, capacity management, and pipeline migration. Reduce manual work and the risk that comes with it;
- Work across engineering teams. Partner with ML Platform, ML Engineering, Analytics, Security, and Cloud Infrastructure to make new workloads production-ready and reliable at scale.