Manulife Financial
Full-Stack Software Engineer
- Compensation
- Not disclosed
- Location
- Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Fullstack
- Posted
Skills
Must have
- 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
Nice to have
- 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
Full 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.
The ideal candidate has strong SQL skills, experience with cloud‑based data platforms, and a solid understanding of modern ELT/ETL practices.
**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