Data Engineer

About the role

This role owns the design and delivery of enterprise-scale AWS data platforms that power analytics, reporting, AI/ML, and business intelligence. The focus is on building dependable data foundations that improve how the business accesses, transforms, and uses information.

Responsibilities

• Own the design and implementation of AWS-native data platforms that support enterprise analytics and reporting. Ensure the platform is built for scale, reliability, and practical business use.
• Build and optimize ETL and ELT pipelines using AWS services so data flows are efficient, resilient, and ready for downstream consumption. Improve delivery speed while reducing manual effort and failure points.
• Architect data lake and lakehouse solutions that create a governed foundation for diverse data use cases. Enable teams to access trusted data for intelligence, AI/ML, and business decision-making.
• Lead Spark and distributed processing initiatives to handle large and complex datasets effectively. Improve throughput, performance, and engineering consistency across workloads.
• Implement data governance, security, and quality standards across the platform. Reduce risk, strengthen trust in the data, and support compliant enterprise usage.
• Support AI/ML data engineering and feature engineering needs so data assets are ready for advanced analytics. This ensures models and downstream teams can work with reliable, structured inputs.
• Drive DevOps, Infrastructure as Code, and CI/CD practices to improve automation and deployment discipline. The outcome is faster delivery with better stability and repeatability.
• Optimize performance, scalability, and reliability across data systems and workflows. Keep the platform efficient as data volumes and business demand grow.

Requirements

The Lead AWS Data Engineer will combine hands-on engineering with technical leadership, guiding teams toward scalable architecture, stronger automation, and higher platform reliability. Success in this role means turning complex data requirements into secure, governed high-performing solutions that support both current workloads and future growth.

The position requires deep AWS expertise, strong programming and data engineering skills, and the ability to influence delivery standards across engineering teams.

Essential Skills & Technologies

• Deep hands-on experience with AWS data services such as S3, Glue, Lambda, EMR, Redshift, Athena, and Lake Formation. These tools should be used to build and govern modern data platforms.
• Strong Python, PySpark, and SQL skills for developing data pipelines, transformation logic, and performance-tuned processing. The role depends on clean, maintainable engineering code.
• Practical experience with Spark, workflow orchestration, and distributed computing for large-scale processing. You should know how to design jobs that are reliable, efficient, and operationally manageable.
• Working knowledge of Terraform or CloudFormation for Infrastructure as Code. This supports repeatable environments, better control, and more disciplined cloud operations.
• Strong understanding of data governance, security, quality, and modern data architecture patterns. These principles must guide platform decisions and delivery choices.
• Experience supporting AI/ML workloads and feature engineering pipelines. The role requires data foundations that can serve advanced analytics use cases.
• Familiarity with DevOps practices, CI/CD pipelines, and cloud-native delivery methods. These capabilities help teams move quickly without sacrificing stability.

Plus

• Experience mentoring engineers or leading technical delivery across data teams. This adds leverage by improving team standards and execution quality.
• Exposure to lakehouse architecture and enterprise-scale transformation programs. This helps the candidate contribute faster in complex, evolving environments.
• Experience in collaborative global delivery setups where cross-functional coordination is important. This supports smoother execution across multiple stakeholders.

Qualifications

Prioritize hands-on experience with AWS data services, strong programming and data engineering skills, and the ability to influence delivery standards across engineering teams.

Skills

• Deep hands-on experience with AWS data services such as S3, Glue, Lambda, EMR, Redshift, Athena, and Lake Formation.
• Strong Python, PySpark, and SQL skills for developing data pipelines, transformation logic, and performance-tuned processing.
• Practical experience with Spark, workflow orchestration, and distributed computing.
• Working knowledge of Terraform or CloudFormation for Infrastructure as Code.
• Strong understanding of data governance, security, quality, and modern data architecture patterns.
• Experience supporting AI/ML workloads and feature engineering pipelines.
• Familiarity with DevOps practices, CI/CD pipelines, and cloud-native delivery methods.