Senior Data Engineer — Scale Pipelines, Drive Data Decisions

Grab; Petaling Jaya; On-site; MYR 368,000 - 613,000; Full time

Benefits offered by this job

Term Life Insurance

Medical Insurance

GrabFlex

Parental leave

Birthday leave

LASA volunteering leave

Grabber Assistance Programme

FlexWork

Job summary

Grab is seeking a Senior Data Engineer for its onsite data engineering team in Southeast Asia. You will build and industrialize data pipelines, collaborate with product and analytics stakeholders, and shape data architecture to support reporting, dashboards, and data science use cases.

You will manage end-to-end pipelines (ETL, streaming, batch) using Python and SQL, and work with technologies like PySpark and Airflow. Strong communication and multi-project leadership are essential.

Qualifications

• 4+ years of experience in data engineering.
• Strong Python and SQL skills.
• Experience with PySpark, Presto, and Airflow.
• Fluency in written and spoken English.
• Ability to work directly with business stakeholders to scope and deliver data solutions.

Responsibilities

• Leverage large volumes of data from internal sources across the business.
• Develop, maintain, and support production data pipelines (ETL, streaming, batch) using Python and SQL.
• Act as the engineering point of contact for business stakeholders, gathering and assessing their data needs.
• Ensure data architecture meets business and reporting requirements, including SLA commitments.
• Recommend ways to deliver quality data more efficiently and identify opportunities to add value.
• Run multiple concurrent pipeline projects and migrations from scoping through delivery.
• Work with backend engineers on source-system migrations to ensure no data loss or SLA breach.

Skills

Python

SQL

PySpark

Airflow

ETL

Data warehousing

English fluency

Stakeholder communication

Tools

Presto

Airflow

Job description

Grab is seeking a Senior Data Engineer for its onsite data engineering team in Southeast Asia. You will build and industrialize data pipelines, collaborate with product and analytics stakeholders, and shape data architecture to support reporting, dashboards, and data science use cases.

You will manage end-to-end pipelines (ETL, streaming, batch) using Python and SQL, and work with technologies like PySpark and Airflow. Strong communication and multi-project leadership are essential.