Data Engineer: Azure Databricks & Power BI Pipelines

EPAM Systems; Malaysia; On-site; MYR 80,000 - 130,000; Full time

Job summary

EPAM Systems in Malaysia is seeking a Data Engineer (Databricks) to build reliable pipelines and analytics-ready datasets on Azure for Power BI reporting and digital analytics use cases. You will take ownership from external vendors, improve stability and data quality, and partner with BI and product teams to keep data trusted and usable.

Responsibilities include building and maintaining ETL/ELT pipelines with Python, PySpark and SQL in Databricks, ingesting GA4 data, creating analytics-ready

Qualifications

• Databricks development using Python, PySpark and SQL.
• Azure data/analytics context experience.
• GA4 knowledge and data connections validation.
• Strong English communication.

Responsibilities

• Build and maintain ETL/ELT pipelines in Databricks using Python, PySpark and SQL.
• Ingest data from multiple sources including GA4 using reliable patterns.
• Create analytics-ready tables for Power BI dashboards and self-serve analysis.
• Design reporting-ready models including star schemas, facts, dimensions, grain and keys.
• Implement data quality controls including checks for accuracy, completeness, freshness and reconciliation.
• Operate pipelines end to end including monitoring, troubleshooting and backfills.
• Partner with BI developers, analysts and stakeholders to translate reporting needs into data structures.
• Lead knowledge transfer from external teams through documentation and handover planning.

Skills

Databricks

Python

PySpark

SQL

Azure

Data Modeling

Data Quality

English Communication

Job description

EPAM Systems in Malaysia is seeking a Data Engineer (Databricks) to build reliable pipelines and analytics-ready datasets on Azure for Power BI reporting and digital analytics use cases. You will take ownership from external vendors, improve stability and data quality, and partner with BI and product teams to keep data trusted and usable.

Responsibilities include building and maintaining ETL/ELT pipelines with Python, PySpark and SQL in Databricks, ingesting GA4 data, creating analytics-ready