Data Engineer

SKINLAB THE MEDICAL SPA PTE. LTD.; Singapore; On-site; SGD 70,000 - 120,000; Full time

Job summary

SKINLAB THE MEDICAL SPA PTE. LTD.

in Singapore is seeking a Data Engineer to design, build and maintain data pipelines, data structures and the reporting foundation for business intelligence, marketing insights, customer analytics and management decision-making. You will work with IT, Marketing, CRM, Finance and operations to consolidate data from multiple systems into a reliable analytics-ready environment.

Qualifications

• Diploma or degree in Computer Science, Information Systems, Data Engineering or a related field.
• Approximately 2 to 4 years of relevant experience in data engineering, data integration, business intelligence or analytics engineering.
• Strong working knowledge of SQL and relational databases.

Responsibilities

• Design, build and maintain data pipelines from core business systems and other platforms.
• Consolidate data into structured datasets for reporting, dashboards and analytics.
• Develop and maintain a central data warehouse or data mart.
• Collaborate with business users to translate reporting needs into data models.
• Support customer analytics, campaigns and performance reporting.
• Ensure data quality, governance and documentation across pipelines.

Skills

SQL

Python

ETL/ELT pipelines

Data warehousing

Data governance

Data modelling

Stakeholder communication

Education

Diploma or Degree in Computer Science, Information Systems, Data Engineering or related field

Tools

Azure Data Factory

BigQuery

Power BI

Looker Studio

Tableau

Job description

We are seeking a Data Engineer to build and maintain the data pipelines, data structures and reporting foundation required to support business intelligence, marketing insights, customer analytics and management decision‑making.

The role will work closely with IT, Marketing, CRM, Finance, Operations and external system vendors to consolidate data from multiple business systems into a reliable, structured and analytics‑ready data environment.

This is a technical role, but it requires strong business understanding. The ideal candidate should be able to understand how data is used by management and business teams, especially in areas such as customer behaviour, sales performance, campaign effectiveness, package utilisation, outlet performance and customer retention.

Key Responsibilities

• Design, build and maintain data pipelines from core business systems, CRM, finance, attendance and other operational platforms.
• Consolidate and transform data into structured datasets for reporting, dashboards, marketing insights and management analysis.
• Develop and maintain a central data warehouse, data mart or similar analytics‑ready data environment.
• Work with business, marketing and management users to understand reporting needs and translate them into suitable data models and datasets.
• Support customer analytics, including customer segmentation, retention analysis, package renewal trends and campaign performance reporting.
• Support cross‑outlet business reporting covering sales, customer activity, staff performance, outlet performance and operational KPIs.
• Implement data validation, cleansing and quality checks to improve data accuracy and consistency.
• Support near real‑time or scheduled data integration between business systems.
• Develop and maintain APIs, connectors and automated data workflows where required.
• Prepare reliable datasets for dashboards, business intelligence tools and future AI‑related applications.
• Work with the Applied AI Engineer to prepare clean and well‑structured datasets for AI, machine learning and customer intelligence use cases.
• Implement appropriate access controls, documentation, monitoring and data governance practices.
• Troubleshoot data pipeline failures, integration issues and reporting discrepancies.
• Maintain technical documentation for data sources, pipelines, data models and transformation logic.

Requirements

• Diploma or Degree in Computer Science, Information Systems, Data Engineering, Business Analytics or a related field.
• Approximately 2 to 4 years of relevant experience in data engineering, data integration, business intelligence or analytics engineering.
• Strong working knowledge of SQL and relational databases.
• Experience with Python for data processing, automation or data transformation.
• Experience building ETL or ELT pipelines using APIs, database connections or file‑based data sources.
• Familiarity with data warehouses, data marts or cloud‑based data platforms.
• Good understanding of data modelling, data quality and data governance principles.
• Experience with dashboard or business intelligence tools such as Power BI, Looker Studio, Tableau or equivalent.
• Able to understand business requirements and translate them into practical data solutions.
• Comfortable working with both technical and non‑technical stakeholders.
• Strong analytical, problem‑solving and troubleshooting skills.

Preferred Skills

• Experience with cloud platforms such as Microsoft Azure, Google Cloud Platform or AWS.
• Familiarity with tools such as Azure Data Factory, Microsoft Fabric, BigQuery, Airflow, dbt or equivalent.
• Experience integrating CRM, finance, appointment, sales or operational systems.
• Experience supporting marketing analytics, customer segmentation, campaign reporting or business performance dashboards.
• Understanding of customer lifecycle, retention, campaign effectiveness or sales funnel analysis would be an advantage.
• Understanding of data privacy and security requirements, including Singapore PDPA.
• Exposure to AI, machine learning or customer intelligence projects would be an