AWS Data Engineer

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, Event Bridge, 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 Event Bridge, 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 meta data 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 Sage Maker

• Experience with Salesforce data integration using AWS AppFlow

• Knowledge of Change Data Capture (CDC) and incremental data load patterns