AWS Data Engineer
Posted
Summary
Designs, builds, and operates an end-to-end AWS data lake/lakehouse platform: ingestion pipelines with Glue, Lambda, Step Functions and AppFlow, batch/stream transformations in Redshift, plus security and governance via Lake Formation and IAM. Needs 3-5 years of data engineering experience, strong SQL, and Python or Scala.
Key Responsibilities
Architecture & Design
Design and architect the end-to-end AWS Data Lake and Lakehouse solution, including Landing Zone, Transformed Zone, and Curated/Consumption Zone layers
Define and govern data architecture standards, patterns, and best practices across the platform
Architect reusable data ingestion pipelines supporting REST APIs, JDBC databases, S3 file uploads, and SaaS connectors (e.g. Salesforce via AWS AppFlow)
Design data storage strategies including hot, warm, and cold storage tiers, encryption, and data lifecycle policies
Development & Deployment
Develop and deploy data ingestion pipelines using AWS Glue, Lambda, Step Functions, EventBridge, and API Gateway
Build and maintain data transformation workflows (batch and stream processing) using AWS Glue and Amazon Redshift
Implement orchestration, monitoring, logging, and notification frameworks for pipeline operations
Develop and maintain the AWS Glue Data Catalogue, including schema evolution tracking and metadata tagging
Security & Governance
Configure and enforce data security policies using AWS Lake Formation, IAM, and Secrets Manager
Implement granular access controls at database, table, and column levels
Ensure compliance with data classification, retention, and audit requirements
Support data quality frameworks and observability monitoring
Maintenance & Operations
Monitor platform health, performance, and pipeline reliability
Troubleshoot and resolve data pipeline failures and data quality issues
Maintain documentation for architecture decisions, pipeline configurations, and operational runbooks
Continuously optimise platform performance and cost efficiency on AWS
Requirements Essential
Minimum 3 to 5 years of experience in data engineering, data architecture, or cloud infrastructure roles
Hands‑on expertise with core AWS data services: Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, Amazon Kinesis, AWS Step Functions, Amazon EventBridge, AWS AppFlow, AWS Lake Formation
Strong proficiency in SQL and at least one scripting language (Python or Scala)
Experience designing and implementing Data Lake or Lakehouse architectures
Solid understanding of data governance, data cataloguing, and metadata management
Experience with batch and streaming data processing patterns
AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect certification (or equivalent)
Preferred
Experience integrating with Tableau or similar BI visualisation tools via Amazon Redshift or S3
Familiarity with MLOps frameworks and AI/ML model deployment on AWS SageMaker
Experience with Salesforce data integration using AWS AppFlow
Knowledge of Change Data Capture (CDC) and incremental data load patterns
Prior experience in a government or public sector data environment
Design and architect the end-to-end AWS Data Lake and Lakehouse solution, including Landing Zone, Transformed Zone, and Curated/Consumption Zone layers
Define and govern data architecture standards, patterns, and best practices across the platform
Architect reusable data ingestion pipelines supporting REST APIs, JDBC databases, S3 file uploads, and SaaS connectors (e.g. Salesforce via AWS AppFlow)
Design data storage strategies including hot, warm, and cold storage tiers, encryption, and data lifecycle policies
Development & Deployment
Develop and deploy data ingestion pipelines using AWS Glue, Lambda, Step Functions, EventBridge, and API Gateway
Build and maintain data transformation workflows (batch and stream processing) using AWS Glue and Amazon Redshift
Implement orchestration, monitoring, logging, and notification frameworks for pipeline operations
Develop and maintain the AWS Glue Data Catalogue, including schema evolution tracking and metadata tagging
Security & Governance
Configure and enforce data security policies using AWS Lake Formation, IAM, and Secrets Manager
Implement granular access controls at database, table, and column levels
Ensure compliance with data classification, retention, and audit requirements
Support data quality frameworks and observability monitoring
Maintenance & Operations
Monitor platform health, performance, and pipeline reliability
Troubleshoot and resolve data pipeline failures and data quality issues
Maintain documentation for architecture decisions, pipeline configurations, and operational runbooks
Continuously optimise platform performance and cost efficiency on AWS
Requirements Essential
Minimum 3 to 5 years of experience in data engineering, data architecture, or cloud infrastructure roles
Hands‑on expertise with core AWS data services: Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, Amazon Kinesis, AWS Step Functions, Amazon EventBridge, AWS AppFlow, AWS Lake Formation
Strong proficiency in SQL and at least one scripting language (Python or Scala)
Experience designing and implementing Data Lake or Lakehouse architectures
Solid understanding of data governance, data cataloguing, and metadata management
Experience with batch and streaming data processing patterns
AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect certification (or equivalent)
Preferred
Experience integrating with Tableau or similar BI visualisation tools via Amazon Redshift or S3
Familiarity with MLOps frameworks and AI/ML model deployment on AWS SageMaker
Experience with Salesforce data integration using AWS AppFlow
Knowledge of Change Data Capture (CDC) and incremental data load patterns
Prior experience in a government or public sector data environment