Job Description

Understand business processes, applications, and how data flows from creation through storage and collection.

Design, build, optimize and maintain scalable and reliable data pipelines and ELT/ETL processes on AWS cloud platform.

Design, develop and maintain data models, data marts, and analytical datasets to satisfy growing data needs.

Establish and own the data quality and availability for the various data flows.

Create compelling story and visualisation to convey business insights. Deliver analytics solution to the business.

Perform exploratory data analysis (EDA) to support predictive use cases. 

Leverage existing predictive models (e.g., regression, classification, basic forecasting) on cloud-based ML platforms, performing configuration, parameter tuning and evaluation to meet business requirements.

Be part of a project team to manage and deliver project deliverables, resources and timelines.

Job Requirements

Bachelor’s or equivalent degree in Data Science & Analytics, Computer Science or related field

At least 5 years of experience in data engineering, data science, or related domains

Hands-on experience with SQL, Python, and distributed data systems is required

Experience in ELT/ETL pipeline design, implementation and maintenance

Experience working with visualization tools (AWS Quick would be preferred)

Strong understanding in data architecture, management & governance concepts related to lake-house, data lineage and data quality

Good understanding and experience with cloud services, such as AWS

Knowledge of cloud-based ML platforms (AWS SageMaker, Azure ML, GCP AI Platform) on data preprocessing, feature engineering, and model validation is a plus

Good communication & stakeholder management skills with ability to bridge technical and business requirements

Ability to work independently and manage multiple task assignments with motivation and commitment

Willingness to stay updated on emerging tools, techniques, and best practices in data engineering and analytics.