AWS Data Engineer/Data SME

About the role

The Senior AWS Data Engineer is responsible for designing, building, and supporting scalable data pipelines and curated datasets on AWS. You will work with cross-functional teams to ingest, transform, and serve data for reporting, analytics, and downstream applications. The ideal candidate is hands-on, strong in SQL/Python, and experienced with AWS-native data services and modern data engineering practices.

Key responsibilities

• Design, develop, and maintain end-to-end data pipelines (batch and near real-time) on AWS Data Platform

• Build and manage ETL/ELT workflows using AWS services (e.g., AWS Glue, S3, Redshift, Athena, EMR), dbt and orchestration tools such as Airflow

• Implement data ingestion patterns from diverse sources (databases, APIs, files, event streams) into lake/warehouse layers such as raw, cleansed, and curated data layers

• Develop transformation logic using SQL and Python/PySpark for cleansing, enrichment, and standardisation

• Implement robust data quality checks, reconciliation controls, and monitoring/alerting for failures and anomalies

• Collaborate with data analysts/data scientists to model datasets for analytics and machine learning consumption

• Contribute to DataOps/DevOps practices: version control, CI/CD, automated testing, release management, and operational support

• Produce and maintain technical documentation (data flows, mappings, job schedules, runbooks, and operational procedures)

• Optimise Data Pipeline performance and support workflow orchestration and scheduling

• Support production deployments and operations

About you

• 8–10 years' experience as a Data Engineer

• Advanced SQL skills

• Hands-on experience working with Teradata and Siebel CRM data sets

• Experience delivering data pipelines in a large-scale enterprise data platform environment

• Strong hands-on AWS experience with common data services such as: Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR and dbt

• Strong programming capability in Python and strong data transformation experience using PySpark (preferred) and/or Spark

• Experience with workflow orchestration tools such as Airflow

• Solid understanding of data warehousing concepts (dimensional modelling, partitioning, incremental loads, CDC concepts)

• Experience implementing monitoring, logging, alerting, and operational support processes

• Strong communication skills and ability to work with stakeholders to translate requirements into data deliverables