Data Engineer

• Build and operate batch and near-real-time data pipelines on our lakehouse platform,
  powering reporting, analytics, and data products across the company.
• Own assigned data domains end to end - from source ingestion to the trusted,
  documented tables that business and technical teams rely on.
• Design and maintain data models that turn raw operational data into reusable,
  well-structured datasets instead of one-off queries.
• Improve pipeline performance, reliability, and infrastructure cost as data volume and
  business complexity grow.
• Establish and maintain data quality standards, including validation, freshness monitoring,
  and alerting on critical datasets.
• Keep metadata, lineage, and documentation up to date so data consumers can find and
  trust the data they use.
• Support BI, Analytics, and Data Science teams by preparing serving datasets and
  resolving data issues they raise.
• Partner with Backend and Product teams to define event and CDC data contracts for
  new and existing services.
• Participate in the team's on-call rotation; investigate incidents, restore data SLAs, and
  drive follow-up improvements.
• Contribute to code review, technical documentation, and the team's deployment and
  engineering practices.

• 2+ years of hands-on experience in Data Engineering, or an equivalent role with
  ownership of production data pipelines.
• Strong SQL, including window functions, complex CTEs, and query optimization on
  tables with hundreds of millions of rows.
• Production-level Python: structured, tested, and packaged code - not only notebooks or
  one-off scripts.
• Hands-on Spark experience (PySpark or Scala): partitioning, shuffle, join strategies, data
  skew, and the ability to read and interpret execution plans.
• Practical experience with a workflow orchestrator; Airflow strongly preferred.
• Solid understanding of data warehouse and lakehouse fundamentals: dimensional
  modeling, incremental loads, CDC handling, snapshot vs delta processing.
• Comfortable with Git and CI/CD workflows as part of daily development.
• Working knowledge of Docker and Kubernetes: able to containerize a job, read pod logs
  and events, understand resource requests and limits, and debug a failing or evicted pod.
• Strong debugging discipline: reads logs and metrics, isolates variables, and verifies
  hypotheses with data instead of guessing.
• Able to read English technical documentation and open-source code independently.

Nice to have

• Production experience with Apache Iceberg, Delta Lake, or Hudi, including snapshot,
  manifest, and metadata layer concepts.
• Experience with Trino/Presto, or OLAP engines such as Apache Doris, ClickHouse, or
  StarRocks.
• Deeper Kubernetes experience: Helm, ArgoCD/GitOps, and resource tuning for Spark
  executors.
• Streaming experience with Spark Structured Streaming or Flink, including state,
  watermark, and exactly-once semantics.
• Familiarity with data catalog and quality tooling such as DataHub, OpenMetadata, dbt, or
  Great Expectations.
• Experience with Vault, External Secrets Operator, Terraform, or Terragrunt.
• Exposure to logistics, marketplace, mobility, e-commerce, or on-demand platform data.
• Experience with billing, reconciliation, or finance data pipelines.

• Physical Wellbeing Benefit: General Insurance, Medical check-up, Accident Insurance, Healthcare Insurance.
• Emotional Wellbeing Benefit: Company Trip, Year End Party, Aha Hour Activities, Special Day Gifts, Aha Club (Badminton, Soccer).
• Financial Wellbeing Benefit: Grab/Be For Work (Tech/Lead Level), Workplace Relocation, 13th Month Salary, PP Appreciate, Annual Leave Remain.