We are looking for a skilled and self-motivated ETL Development Engineer with strong hands-on experience in high-throughput data pipelines and offline batch processing. In this role, you will design, develop, and optimize scalable data warehouse models and Spark-based ETL workflows. You will work closely with cross-functional teams to ensure high data availability, seamless task orchestration, and reliable data architecture delivery.

Responsibilities

- ETL Pipeline Development: Design, build, and maintain high-throughput offline batch ETL pipelines utilizing Apache Spark (Core/SQL).
- Performance Tuning: Troubleshoot, benchmark, and optimize Spark jobs, memory allocation, and query execution for high performance and cost efficiency.
- Data Warehouse Modeling: Architect and implement scalable data warehouse dimensional models (Kimball/Inmon methodologies, ODS/DWH/ADS layering) supporting analytical workflows.
- Data Warehouse Modeling: Architect and implement scalable data warehouse dimensional models (Kimball/Inmon methodologies, ODS/DWH/ADS layering) supporting analytical workflows.
- Pipeline Orchestration: Manage DAG workflows, incremental processing, failure retry logic, and real-time monitoring/alerting using orchestration platforms such as Apache Airflow.
- Code Quality & Best Practices: Maintain high software engineering standards through clean code writing, robust documentation, code reviews, and automated unit testing.
- Migration & Modernization: Assist in or lead large-scale ETL re-engineering, platform migrations, and optimization initiatives across legacy and modern data stacks.
- Cross-functional Collaboration: Read technical documentation, communicate seamlessly with international tech teams in English, and deliver features independently from requirements to production.

Requirements

- Bachelor's degree or above in a computer-related major
- Experience:



3+ years in PySpark offline ETL development
- Technical Skills: Spark Core/SQL tuning, Airflow orchestration, and data warehouse modeling.
- Domain & Infra: Experience or familiarity with cloud environment workflows and telco data processing challenges.
- Delivery: Strong coding standards and proven independent delivery capability.
- Language: Working proficiency in English (technical documentation, written, and spoken).
- Preferred: Prior experience in large-scale ETL migration or reengineering in telco/cloud environments.
- Proven hands-on experience in executing end-to-end data and logic-level migrations from legacy ETL systems (specifically Ab Initio) to modern frameworks using PySpark
- Strong background in complex data mapping, table design, and script rewriting, preferably within large-scale Data Migration projects in the Telecommunications or Banking sectors

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Bachelor's degree in Computer Science or related field
PySpark, ETL development, Performance tuning, Data warehouse modeling, Pipeline orchestration, Code quality, Migration & modernization, Cross-functional collaboration, English communication
ETL Pipeline Development: design, build, and maintain high-throughput offline batch ETL pipelines using Spark., Performance Tuning: optimize Spark jobs, memory, and query execution for performance and cost efficiency., Data Warehouse Modeling: architect scalable dimensional models for analytical workflows., Pipeline Orchestration: manage DAG workflows using Airflow with monitoring and alerts., Code Quality & Best Practices: ensure clean code, documentation, reviews, and unit testing., Migration & Modernization: participate in or lead ETL re-engineering and platform migrations., Cross-functional Collaboration: read docs and communicate with international teams in English.

📌 ETL Developer (Asia)
🏢 Merkleinnovation
📍 Asia