Take end-to-end ownership of our cloud data architecture—designing, developing, and implementing a robust data warehouse using AWS services such as S3, Glue, Redshift, Lambda, Step Functions etc.
Lead the evolution of our data infrastructure with a long-term vision, ensuring scalability, reliability, and performance.
Define and enforce high standards across data engineering—driving excellence in source control, automation, testing, and deployment.
Ensure data integrity, governance, and security are embedded throughout the pipeline, delivering datasets stakeholders can depend on.
Promote engineering best practices through clean, well-documented code, peer reviews, and strong CI/CD workflows.
Act as a trusted technical mentor, growing the skillset of your team and raising the bar on data engineering quality.
Design and maintain high-performance ETL/ELT pipelines to rapidly transform raw data into ready-to-use, structured datasets.
Continuously optimize data models (e.g., star schema) for analytics and reporting, accelerating decision-making across the business.
Embrace agility—identify inefficiencies, ship improvements quickly, and iterate with speed and precision.
Requirements :
Minimum 7 years of experience in data engineering, with at least 3 years working in cloud-based environments (preferably AWS).
Strong hands-on experience with AWS S3, Glue, Redshift, Lambda, Step Functions, and other core AWS services.
Proficient in SQL and Python for data transformation and automation.
Strong communication and collaboration skills to work with cross-functional teams.
Experience in building and managing data models and data pipelines for large-scale data environments.
Solid understanding of data warehousing principles, data lakes, and modern data architecture.
Experience leading and mentoring data engineering teams.
Interested candidates please click
Apply .
Please note that only shortlisted candidates will be notified.
EA Registration No: R Links HR Singapore Pte Ltd | EA License No: 09C5322
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