Role Overview :

We are seeking a highly skilled AWS Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data solutions.

The ideal candidate should have hands-on expertise in AWS cloud services, data lake/warehouse, ETL/ELT pipelines, and big data frameworks to support analytics and business insights.

What you do :

- Design, build, and optimize data pipelines for structured/unstructured data.

- Develop and manage ETL/ELT workflows using AWS services (Glue, Lambda, EMR, Kinesis).

- Work with data storage solutions such as S3, Redshift, DynamoDB, RDS.

- Implement streaming & batch data processing with Spark, Kinesis, Kafka.

- Ensure data quality, governance, and security standards across systems.

- Collaborate with Data Scientists, Analysts, and stakeholders to enable self-service analytics and ML pipelines.

- Monitor, troubleshoot, and improve pipeline performance.

- Apply CI/CD and Infrastructure as Code practices for deployment (Terraform, CloudFormation, Jenkins).

What we expect :

- Strong programming in Python, PySpark, or Scala.

- Hands on experience with SageMaker

Hands-on experience with AWS services :

- Compute & Storage : S3, EC2, Lambda, EMR

- Data Integration : Glue, Kinesis, Step Functions

- Databases/Warehousing : Redshift, RDS, DynamoDB

- Knowledge of data modeling, schema design, data lakes, and data warehouses.

- Experience with big data frameworks (Spark, Hadoop, Kafka).

- Proficiency in SQL & database optimization.

- Knowledge of CI/CD pipelines, Git, DevOps practices.

Good to Have :

- Familiarity Databricks, or ML pipelines.

- Exposure to BI tools (QuickSight, Tableau, Power BI).

- Containerization & orchestration (Docker, Kubernetes, EKS).