Role Overview :

As an AWS Data Engineer, you will play a pivotal role in architecting and maintaining robust data pipelines that power our clients' analytical ecosystems. You will work closely with cross-functional teams, including data scientists, product managers, and software architects, to transform raw data into actionable business intelligence. This position is critical in ensuring the scalability, reliability, and security of data infrastructure, directly influencing the strategic outcomes and operational efficiency of the organizations we support.

Key Responsibilities :

- Design and implement scalable data ingestion and processing pipelines using AWS Glue and PySpark to handle large-scale datasets efficiently.

- Develop and optimize serverless data processing workflows using AWS Lambda to ensure cost-effective and event-driven data operations.

- Manage and tune distributed computing clusters using AWS EMR to support complex analytical workloads and high-performance computing requirements.

- Architect and maintain high-performance data warehouses in Amazon Redshift to facilitate rapid querying and reporting for business stakeholders.

- Orchestrate containerized applications and services using Amazon ECS and Amazon EC2 to ensure high availability and seamless deployment cycles.

- Collaborate with engineering teams to troubleshoot data bottlenecks and implement performance tuning strategies across the entire AWS data stack.

Required Skillset :

- Demonstrated expertise in building end-to-end data engineering solutions within the AWS ecosystem, with a strong command over Python for data manipulation and automation.

- Proven ability to design and optimize data models in Amazon Redshift, ensuring data integrity and query performance for complex analytical needs.

- Strong proficiency in managing cloud infrastructure, specifically configuring and scaling Amazon EC2 instances and ECS clusters to meet evolving business demands.

- Excellent communication skills with the ability to translate complex technical requirements into clear, actionable documentation for non-technical stakeholders.

- A collaborative mindset, capable of working effectively in hybrid or remote environments while maintaining high standards of code quality and peer review.

- A Bachelor's or Master's degree in Computer Science, Information Technology, or a related quantitative field, supported by 7 to 12 years of hands-on experience in data engineering roles.