Full-Stack Software Engineer

Manulife John Hancock; Kuala Lumpur; Hybrid; MYR 110,000 - 180,000; Full time

Benefits offered by this job

Hybrid work

Job summary

Manulife John Hancock seeks a Data Engineer with 2–3 years of hands-on experience to design, build, and optimize data pipelines and platforms supporting analytics, BI, and downstream applications. You will collaborate with analytics, data science, and product teams to ensure high-quality, scalable data for reporting.

The role requires strong SQL, Python, cloud data platforms (AWS/Azure/GCP), and experience with ETL/ELT tools (dbt, Airflow, Fivetran, or Azure Data Factory).

Qualifications

• Bachelor's degree in CS/Engineering/Data Science or related field.
• 2–3 years of professional Data Engineering experience.
• Strong SQL with complex queries and window functions.
• Python or similar language for data processing.
• Hands-on with cloud data platforms (AWS/Azure/GCP).
• Familiarity with data warehouses (Snowflake/BigQuery/Redshift/Synapse).
• Experience with ETL/ELT tools (dbt, Airflow, Fivetran, Azure Data Factory).
• Understanding of data modeling concepts (dimensional modeling).
• Experience with version control (Git).
• Strong problem-solving and collaboration skills.

Responsibilities

• Design, build, and maintain reliable data pipelines for batch and near real-time processing.
• Develop and manage ELT/ETL workflows to ingest data from multiple sources.
• Build and optimize data models to support analytics, BI, and reporting.
• Ensure data quality through validation, monitoring, and testing.
• Collaborate with analysts, data scientists, and product teams to understand data requirements.
• Troubleshoot and resolve data pipeline issues and performance bottlenecks.
• Maintain documentation for pipelines, models, and processes.
• Follow security, access control, and cost optimization best practices.

Skills

SQL

Python

Cloud platforms

Data modeling

Git

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

dbt

Airflow

Fivetran

Azure Data Factory

Job description

We are seeking a Data Engineer with 2-3 years of hands‑on experience in building, maintaining, and optimizing data pipelines and data platforms. This role will work closely with analytics, data science, and business teams to ensure high‑quality, reliable, and scalable data for reporting, insights, and downstream applications.

Position Responsibilities

• Design, build, and maintain reliable and scalable data pipelines for batch and (where applicable) near real‑time data processing
• Develop and manage ELT/ETL workflows to ingest data from multiple sources (databases, APIs, files)
• Build and optimize data models to support analytics, BI, and reporting use cases
• Ensure data quality, accuracy, and consistency through validation, monitoring, and testing
• Collaborate with analysts, data scientists, and product teams to understand data requirements
• Troubleshoot and resolve data pipeline failures and performance issues
• Maintain documentation for data pipelines, data models, and operational processes
• Follow best practices in security, access control, and cost optimization for data platforms

Required Qualifications

• Bachelor's Degree in Computer Science, Engineering, Data Science, or a related field
• 2-3 years of professional experience in Data Engineering or related roles
• Strong proficiency in SQL (complex queries, joins, window functions)
• Experience with Python (or similar language) for data processing and automation
• Hands‑on experience with cloud data platforms (AWS, Azure, or GCP)
• Familiarity with data warehouses (e.g., Snowflake, BigQuery, Redshift, Synapse)
• Experience with ETL/ELT tools or frameworks (dbt, Airflow, Fivetran, Azure Data Factory, etc.)
• Understanding of data modeling concepts (dimensional modeling, fact & dimension tables)
• Experience working with version control (Git)
• Good problem‑solving, communication, and collaboration skills

Preferred Qualifications

• Experience with distributed processing frameworks (Spark / Databricks)
• Exposure to streaming data (Kafka, Event Hubs, Kinesis)
• Knowledge of data quality or observability tools
• Basic understanding of DevOps/IaC (Terraform, CI/CD pipelines)
• Experience supporting machine learning or GenAI data pipelines

When You Join Our Team

• We'll empower you to learn and grow the career you want.
• We'll recognize and support you in a flexible environment where well‑being and inclusion are more than just words.
• As part of our global team, we'll support you in shaping the future you want to see.

About Manulife and John Hancock

Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html .

Manulife is an Equal Opportunity Employer

At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals. We are committed to fair recruitment, retention, advancement and compensation, and we administer all of our practices and programs without discrimination on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, religion or religious beliefs, creed, sex (including pregnancy and pregnancy‑related conditions), sexual orientation, genetic characteristics, veteran status, gender identity, gender expression, age, marital status, family status, disability, or any other ground protected by applicable law.It is our priority to remove barriers to provide equal access to employment. A Human Resources representative will work with applicants who request a reasonable accommodation during the application process. All information shared during the accommodation request process will be stored and used in a manner that is consistent with applicable laws and Manulife/John Hancock policies. To request a reasonable accommodation in the application process, contact [e-mail] .

Working Arrangement

Hybrid