Data Engineer (Data Platforms)
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Summary
A hands-on Data Engineer supports a central data platform: building and maintaining ETL/ELT pipelines, SQL and Python transformations, data quality/reconciliation checks, and governed datasets for reporting and analytics. Core stack is SQL, Python and AWS (S3, Athena, IAM), with Spark/Databricks and Tableau as advantages. Role is in Singapore, working with technical teams, vendors and business use
Our client is seeking a hands-on
Data Engineer
to support the operations, maintenance and enhancement of a central data platform. You will work closely with technical teams, source system owners, vendors and business users to build reliable data pipelines and deliver quality datasets for reporting and analytics. Responsibilities Develop and maintain ETL/ELT pipelines and file-based interfaces for data ingestion. Build SQL and Python solutions for data cleansing, transformation, standardisation and reconciliation. Maintain data models, tables, datasets and processing logic to support new requirements. Implement data quality, validation and reconciliation checks, investigate anomalies and resolve root causes. Monitor and troubleshoot data pipelines, perform reruns and improve pipeline reliability and performance. Support system enhancements, testing, UAT, defect resolution and production issues. Develop governed datasets, queries and data sources for downstream reporting and analytics. Translate business requirements into data mappings and technical solutions, while maintaining clear documentation. Support data governance, audit, access review and compliance activities.
Requirements 3–4 years of relevant Data Engineering experience, with strong SQL and working proficiency in Python for data processing, automation and pipeline development. Hands-on experience in ETL/data pipeline development, including data ingestion, transformation, validation and production support. Strong understanding of data modelling, data warehousing and relational/file-based data. Experience handling complex, inconsistent or legacy data, including data reconciliation, troubleshooting and root-cause analysis. AWS experience preferred, particularly S3, Athena, IAM, CSV/Parquet and partitioned datasets; experience with other cloud data platforms will also be considered. Experience with Spark, Databricks or similar technologies is an advantage. Experience with Tableau or other BI tools is a plus. Strong analytical, problem-solving and stakeholder management skills, with the ability to work independently and deliver quality work within timelines.
Data Engineer
to support the operations, maintenance and enhancement of a central data platform. You will work closely with technical teams, source system owners, vendors and business users to build reliable data pipelines and deliver quality datasets for reporting and analytics. Responsibilities Develop and maintain ETL/ELT pipelines and file-based interfaces for data ingestion. Build SQL and Python solutions for data cleansing, transformation, standardisation and reconciliation. Maintain data models, tables, datasets and processing logic to support new requirements. Implement data quality, validation and reconciliation checks, investigate anomalies and resolve root causes. Monitor and troubleshoot data pipelines, perform reruns and improve pipeline reliability and performance. Support system enhancements, testing, UAT, defect resolution and production issues. Develop governed datasets, queries and data sources for downstream reporting and analytics. Translate business requirements into data mappings and technical solutions, while maintaining clear documentation. Support data governance, audit, access review and compliance activities.
Requirements 3–4 years of relevant Data Engineering experience, with strong SQL and working proficiency in Python for data processing, automation and pipeline development. Hands-on experience in ETL/data pipeline development, including data ingestion, transformation, validation and production support. Strong understanding of data modelling, data warehousing and relational/file-based data. Experience handling complex, inconsistent or legacy data, including data reconciliation, troubleshooting and root-cause analysis. AWS experience preferred, particularly S3, Athena, IAM, CSV/Parquet and partitioned datasets; experience with other cloud data platforms will also be considered. Experience with Spark, Databricks or similar technologies is an advantage. Experience with Tableau or other BI tools is a plus. Strong analytical, problem-solving and stakeholder management skills, with the ability to work independently and deliver quality work within timelines.