Data Engineer
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Summary
Singapore-based Data Engineer who designs and maintains data pipelines, ETL processes, and data quality routines, builds dashboards for stakeholders, and explores ML/statistical techniques under guidance. Requires 8+ years in data platforms with Python/SQL and tools like Airflow, Azure Data Factory, Databricks, and Snowflake.
KEY RESPONSIBILITES (JOB REQUIREMENTS)
Data Engineering & Platform Support
Design and maintain basic data pipelines and integration processes to support reliable data flow and ensure data availability for analytics.
Implement and monitor data validation and cleansing routines to improve data quality and ensure accurate reporting.
Support and troubleshoot operational issues in data pipelines and databases to maintain smooth and continuous platform performance.
Develop and update dashboards and reports to visualize key metrics and support data-driven decision-making across teams.
Collaboration & Business Understanding Collaborate with stakeholders to gather data requirements and ensure solutions align with business needs and objectives. Communicate data insights and findings in a clear and actionable manner to support informed decision-making by non-technical stakeholders. Assist in analyzing structured and unstructured data to identify trends and patterns that contribute to solving business problems.
Learning & Continuous Improvement Research and apply suitable machine learning models or statistical techniques under guidance
to support business use cases. • Stay updated on industry trends and tools in data engineering and analytics to enhance technical skills and contribute to team innovation. • - Recommend and support process improvements to increase data pipeline efficiency and improve data reliability over time.
JOB REQUIREMENTS/JOB QUALIFICATIONS Bachelor's degree in Computer Science, Data Science, Statistics, or a related field. At least 8 years' working experience in data platform, data analytics or relevant projects. Proven experience as a Data Engineer, with design and managing enterprise data architecture, including batch and real time processing. Solid programming skills in languages such as Python, SQL, SAS, or R for data analysis and data engineering tasks. Prior experience as a Data Engineer, with a demonstrated ability to design and implement data models, ETL processes, and data integration solutions. Proven experience in at least one major data processing tool (Informatica, Kettle, Talend, Airflow, Dolphin etc.). Prior experience with cloud-based data platforms (e.g., Azure Data Factory, Azure Purview, Databricks, Snowflake, Kafka, or equivalent) and data storage technologies (e.g., SQL/NoSQL, Vector and Graph etc.). Excellent problem-solving skills and the ability to work with large, complex data sets. Strong attention to detail, ensuring data accuracy and reliability in all analyses. Effective communication skills to convey complex technical concepts to non-technical stakeholders. Proven ability to work independently as well as collaboratively in a fast-paced, team-oriented environment. Prior experience of AI technology stack is a great plus. Prior experience in at least one BI tool (Power BI, Tableau, etc.). Related industry experience is a plus.
CORE COMPETENCY Data Analytics Design thinking Business Needs Analysis Data Engineering Data Quality Assurance Data Security & Compliance
Collaboration & Business Understanding Collaborate with stakeholders to gather data requirements and ensure solutions align with business needs and objectives. Communicate data insights and findings in a clear and actionable manner to support informed decision-making by non-technical stakeholders. Assist in analyzing structured and unstructured data to identify trends and patterns that contribute to solving business problems.
Learning & Continuous Improvement Research and apply suitable machine learning models or statistical techniques under guidance
to support business use cases. • Stay updated on industry trends and tools in data engineering and analytics to enhance technical skills and contribute to team innovation. • - Recommend and support process improvements to increase data pipeline efficiency and improve data reliability over time.
JOB REQUIREMENTS/JOB QUALIFICATIONS Bachelor's degree in Computer Science, Data Science, Statistics, or a related field. At least 8 years' working experience in data platform, data analytics or relevant projects. Proven experience as a Data Engineer, with design and managing enterprise data architecture, including batch and real time processing. Solid programming skills in languages such as Python, SQL, SAS, or R for data analysis and data engineering tasks. Prior experience as a Data Engineer, with a demonstrated ability to design and implement data models, ETL processes, and data integration solutions. Proven experience in at least one major data processing tool (Informatica, Kettle, Talend, Airflow, Dolphin etc.). Prior experience with cloud-based data platforms (e.g., Azure Data Factory, Azure Purview, Databricks, Snowflake, Kafka, or equivalent) and data storage technologies (e.g., SQL/NoSQL, Vector and Graph etc.). Excellent problem-solving skills and the ability to work with large, complex data sets. Strong attention to detail, ensuring data accuracy and reliability in all analyses. Effective communication skills to convey complex technical concepts to non-technical stakeholders. Proven ability to work independently as well as collaboratively in a fast-paced, team-oriented environment. Prior experience of AI technology stack is a great plus. Prior experience in at least one BI tool (Power BI, Tableau, etc.). Related industry experience is a plus.
CORE COMPETENCY Data Analytics Design thinking Business Needs Analysis Data Engineering Data Quality Assurance Data Security & Compliance