Senior Data Engineer

G-Asiapacific; Subang Jaya; On-site; MYR 90,000 - 180,000; Full time

Job summary

G-Asiapacific in Malaysia is seeking a Mid-Level Data Engineer with 3–5 years of hands-on AWS experience to design practical data solutions and engage with customers. You will design data pipelines, work with Sales, Solution Architects, Technology and Delivery to ensure successful outcomes.

The role combines data engineering with consulting, requiring strong SQL, Python and Spark skills, plus a customer‑focused mindset for presales and delivery.

Qualifications

• 3–5 years of Data Engineering experience.
• Hands-on with AWS data services.
• Strong SQL and Python skills.
• Experience with data modeling and data warehousing.
• Familiarity with Spark/PySpark and data governance is a plus.
• Excellent communication and stakeholder management.

Responsibilities

• Design, develop and optimize data pipelines, ETL/ELT, data warehouses and data lakes.
• Hands-on with AWS data services, including Redshift, S3, Glue, Lambda, Athena, EMR/Spark.
• Develop data solutions using SQL, Python, PySpark/Spark and modern data engineering practices.
• Conduct Data Discovery, Data Assessment and Data Workshops with customers.
• Analyze customer data environments and recommend consolidation, integration, migration and modernization.
• Support presales activities from discovery sessions to customer presentations.
• Coordinate with Sales, SA, Technology and Delivery for smooth handover and scalable solutions.
• Support ML/AI data engineering needs by preparing data pipelines and data platforms.

Skills

AWS

SQL

Python

Data pipelines

ETL/ELT

Data modeling

Data warehouses

Data lakes

Spark / PySpark

Communication

Tools

Redshift

S3

Glue

Lambda

Athena

EMR / Spark

Job description

We are looking for a Mid-Level Data Engineer with 3–5 years of solid experience, primarily in AWS, who can combine hands‑on data engineering with customer‑facing consulting and presales‑to‑delivery engagement. The role requires someone who can understand customer challenges, assess their data landscape, design practical solutions, and work closely with Sales, Solution Architects (SA), Technology and Delivery teams to ensure successful outcomes.

Key Responsibilities:

• Design, develop and optimize data pipelines, ETL/ELT processes, data warehouses and data lakes.
• Strong hands‑on experience with AWS data services, especially Amazon Redshift, S3, Glue, Lambda, Athena, EMR/Spark and related services.
• Develop data solutions using SQL, Python, PySpark/Spark and modern data engineering practices.
• Conduct Data Discovery, Data Assessment and Data Workshops with customers to understand current architecture, data sources, data quality and business requirements.
• Analyze customer data environments and recommend approaches for data consolidation, integration, migration, modernization and analytics.
• Support presales activities, including discovery sessions, solution discussions, technical proposals, effort estimation, POCs and customer presentations.
• Navigate the customer journey from Presales → Solution Architecture → Engineering → Delivery/Post‑Sales, ensuring smooth handover and alignment.
• Work with Sales, SA, Technology and Delivery teams to translate business challenges into feasible and scalable technical solutions.
• Support Machine Learning / AI use cases by preparing data pipelines, datasets and data platforms required for model development and deployment.
• Troubleshoot, optimize and improve data workloads for performance, scalability, reliability and cost.
• Remain customer‑focused and ensure that the proposed technology ultimately solves the customer’s business and technical challenge.

Required Skills:

• 3–5 years of Data Engineering experience with strong hands‑on delivery capability.
• Strong AWS experience, particularly Amazon Redshift and AWS data/analytics services.
• Strong SQL and Python skills with practical ETL/ELT and data pipeline experience.
• Good understanding of data modeling, data warehousing, data lakes, data integration and data quality.
• Experience with Spark/PySpark is preferred.
• Exposure to Machine Learning / AI data engineering requirements.
• Strong analytical, problem‑solving, communication and stakeholder‑management skills.
• Comfortable conducting customer workshops and presenting technical solutions.

Good to have:

• Experience with Microsoft Azure and/or Google Cloud Platform (GCP).
• Exposure to Azure Synapse, Data Factory, Databricks, BigQuery, Dataflow or equivalent technologies.
• Experience in consulting, system integration or customer‑facing technology roles.
• Knowledge of data governance, security, CI/CD and modern cloud data architectures.

Ideal Candidate:

• A hands‑on Data Engineer who can also think like a consultant, which someone who can understand the customer’s problem, assess and consolidate data, design the right solution, support presales, collaborate across Sales/SA/Technology/Delivery, and stay focused on achieving the customer’s desired business outcome.