SKINLAB THE MEDICAL SPA PTE. LTD. Hiring! Full Time Data Engineer in Central Region (Singapore), Earn up to SGD 6,000 - Ricebowl

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