Data Engineer

NSEARCH GLOBAL PTE. LTD.; Singapore; On-site; SGD 70,000 - 120,000; Full time

Job summary

NSEARCH GLOBAL PTE. LTD. in Singapore is seeking a Data Engineer to develop and maintain ETL pipelines ingesting HR data into ODIN. You will build SQL/Python logic for data cleansing, transformation and reconciliation, and enhance data models and data sources for reporting and analytics.

The role includes automating validation checks, monitoring pipelines, and supporting audits and remediation efforts. Prior experience with ETL, data warehousing, and cloud platforms is essential.

Qualifications

• Strong SQL and Python development experience with data pipelines.
• Experience with AWS data services (S3, IAM, Parquet) or similar cloud platforms.
• Familiarity with Spark/Databricks is advantageous but not required for the ODIN environment.
• Hands-on understanding of ETL, data pipeline design and operations, data warehousing and data modelling.

Responsibilities

• Develop and maintain ETL pipelines and file-based interfaces for ingesting HR data into ODIN, including data profiling, mapping, specifications and dataset onboarding.
• Build and maintain SQL and Python logic to cleanse, standardise, transform and reconcile HR data into reliable, reusable datasets.
• Enhance ODIN data models, tables and dependencies to meet new requirements, with traceable mappings, definitions and processing rules.
• Automate validation, reconciliation and cleansing checks; investigate anomalies, trace lineage, resolve root causes and verify fixes.
• Monitor pipelines and interfaces, troubleshoot failures, perform reruns and improve reliability, performance, maintainability and operational documentation.
• Develop governed datasets, Athena tables/views and queries, and Tableau data sources/workbooks for approved reporting and analytics needs.
• Translate stakeholder requirements into data mappings, transformation rules and technical solutions, while documenting decisions and supporting timely delivery.
• Provide technical evidence for audits, access reviews and assurance activities, and support required remediation.

Skills

SQL

Python

ETL pipelines

Data warehousing

Tools

AWS

Parquet

Athena

Databricks

Job description

Our client, one of Asia-Pacific's leading organizations is looking for:

Data Engineer

Responsibilities:

• Develop and maintain ETL pipelines and file-based interfaces for ingesting HR data into ODIN, including data profiling, mapping, specifications and dataset onboarding.
• Build and maintain SQL and Python logic to cleanse, standardise, transform and reconcile HR data into reliable, reusable datasets, including historical and legacy data.
• Enhance ODIN data models, tables and dependencies to meet new requirements, with traceable mappings, definitions and processing rules.
• Support CRs and SRs by reviewing requirements, executing tests, validating outputs, documenting defects and resolving issues through implementation.
• Automate validation, reconciliation and cleansing checks; investigate anomalies, trace lineage, resolve root causes and verify fixes.
• Monitor pipelines and interfaces, troubleshoot failures, perform reruns and improve reliability, performance, maintainability and operational documentation.
• Develop governed datasets, Athena tables/views and queries, and Tableau data sources/workbooks for approved reporting and analytics needs.
• Translate stakeholder requirements into data mappings, transformation rules and technical solutions, while documenting decisions and supporting timely delivery.
• Provide technical evidence for audits, access reviews and assurance activities, and support required remediation.

Requirements:

• Strong proficiency in SQL and working proficiency in Python, with hands-on experience developing data pipelines, transformations, validation and automation.
• Experience with AWS data services such as S3 ingestion, CSV/Parquet, IAM, portioning, and Athena tables, views and queries, or comparable cloud data platforms, is preferred.
• Familiarity with Spark, Databricks or similar modern data engineering technologies is advantageous but not required for the current ODIN environment.
• Hands-on understanding of ETL, data pipeline design and operations, relational and file-based data, data warehousing and data modelling.
• Able to work with complex or legacy source structures and transform them into clean, standardised and reliable datasets for downstream consumption.
• Practical experience implementing data validation and reconciliation checks, investigating discrepancies, tracing data lineage and identifying root causes across source, interface and transformation layers.