[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. • Must possess a Degree / Advanced Diploma in Computer Science, Data Engineering, Software Engineering or equivalent. Seniority will be commensurate with relevant experience and qualifications. • At least 6 years of experience in data engineering or developing data-intensive systems, including the design, build and production support of data pipelines. • Strong hands-on experience with Python and PySpark for large-scale and distributed data processing. • Strong SQL skills, including complex transformations, joins, window functions, query optimisation and performance tuning. • Hands-on experience designing and operating scalable ETL/ELT data pipelines, including incremental loads, retries, backfills, dependency management and failure recovery. • Experience with data modelling for analytical use cases, including dimensional modelling, facts/dimensions, normalization/denormalization and handling historical data. • Hands-on experience with AWS data services relevant to data engineering, such as Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift and Amazon EMR; exposure to Lambda, Kinesis/MSK, DMS or Lake Formation is an advantage where relevant. • Experience with data lake/lakehouse concepts,