Cloud & Data Engineer (AWS)

Job title: Cloud & Data Engineer (AWS)

About the Role

We are seeking a Cloud & Data Engineer to design and build scalable, cloud-native data platforms on AWS. You will partner with cross-functional teams to create data lakes and data warehouses, implement batch and streaming data pipelines, and ensure production-grade reliability and performance.

What You'll Do

• Design, implement and operate cloud-native data lakes and data warehouses on AWS (S3, Glue, Athena, Redshift) to enable analytics at scale.
• Build and maintain batch and streaming data pipelines with a focus on scalability, fault tolerance, and cost efficiency.
• Develop reusable ETL components and utilities; implement robust data modeling, transformations, and schema design.
• Optimize SQL and Python-based data processing for large datasets; leverage PySpark/Spark for processing.
• Implement DevOps practices for data platforms: Infrastructure as Code (Terraform/CloudFormation), CI/CD pipelines; containerization with Docker/ECS/EKS.
• Build and maintain production-grade data workflows; ensure solution quality, performance, and stability; collaborate with stakeholders.
• Work with streaming technologies (Kinesis, Kafka, MSK) and modern data platform concepts such as lakehouse architectures.
• Ensure data governance, data quality, metadata management, and cost optimization (FinOps) in AWS.

What We're Looking For

• Strong hands-on experience with AWS: S3, Glue, Athena, Redshift; cloud-native data lake/warehouse design.
• Deep understanding of batch and streaming data pipelines; scalable and fault-tolerant workflows.
• Proficient in SQL and Python; strong SQL with complex transformations, performance tuning.
• Experience with PySpark/Spark; distributed processing; large-scale datasets in Redshift/Athena.
• Build reusable ETL components; data modeling; transformations; performance optimization.
• Experience with data processing & engineering; structured, semi-structured, and unstructured data.
• Expertise in schema design, partitioning, query optimization.
• DevOps & Platform Engineering: IaC (Terraform/CloudFormation); CI/CD; containerization (Docker, ECS, EKS).
• Collaboration & Ownership: strong ownership mindset; solution quality; production stability; excellent communication and stakeholder collaboration.
• Education: Bachelor's or Master's in Computer Science, Information Systems, Data Engineering or related field.
• Certifications: AWS Certified Data Analytics, AWS Solutions Architect (any two preferred); Plus: Databricks, Snowflake or other cloud data platform certifications.

Nice to Have

• Experience with streaming technologies (Kinesis, Kafka, MSK)
• Exposure to Lakehouse architectures and modern data platforms
• Integration with BI and analytics tools
• Data governance, data quality frameworks, metadata management
• FinOps (cost optimization on AWS)
• Exposure to Marketing/Customer Data Platforms (CDP / MarTech)
• Experience working in Agile delivery models with global teams