[Expression of Interest] Data Engineer

Role Summary

We are looking for a talented and driven engineer to join the Ascend Sandbox team — Experian's cloud-based analytical platform that empowers clients and data scientists to explore, model, and derive insights from data in a secure, governed environment. You will play a key role in building, maintaining, and scaling the platform that serves both internal teams and external clients across the APAC region.

What you’ll be doing  

• Design, build, test, deploy and operate scalable batch and, where applicable, streaming data pipelines for the Ascend Sandbox analytical platform.
• Develop high-quality Python, PySpark and SQL solutions to ingest, transform and process large-scale datasets with accuracy, reliability and efficiency.
• Design data models and reusable, trusted datasets/data products for analytics, data science and downstream applications.
• Engineer and optimise AWS-based data solutions using relevant services such as Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift and Amazon EMR, according to the platform architecture.
• Apply data lake and lakehouse engineering practices, including efficient storage formats, partitioning, schema evolution and storage/query optimisation.
• Design robust ETL/ELT patterns, including incremental processing, dependency management, idempotency, retries, backfills and failure recovery.
• Implement workflow orchestration and scheduling for data pipelines using the platform's approved tooling, such as Airflow/MWAA, AWS Step Functions or AWS Glue Workflows.
• Build automated data quality controls and monitoring for completeness, validity, reconciliation, schema compliance, freshness, pipeline health and processing SLAs/SLOs.
• Optimise Spark workloads and distributed data processing for performance, scalability and cost, including partitioning, joins, shuffles, skew and resource utilisation.
• Implement practical data governance, metadata, lineage, cataloguing, security and access-control requirements across data pipelines and datasets.
• Collaborate with clients, business users, data scientists and technical teams to translate business and data requirements into scalable data-processing solutions and integrations.
• Investigate and resolve production data and pipeline issues, perform root-cause analysis, and implement preventive improvements to reliability and operational efficiency.
• Ensure data engineering solutions comply with established architecture, coding, testing, security and quality standards, and contribute to code reviews, CI/CD and automated deployment practices.

Department: Information Technology & Systems
Function: Administrative
Experience Level: Associate