In this article we will learn about a very high paying job in IT Industry and how will you achieve this. So on an average with 7 to 8 years of experience guy can make around 30-40LPA in data engineering. So lets start a roadmap to become an successful AWS Data engineer.

Steps and Technologies used to become Senior AWS Data Engineer:

1) Developing Data Engineering Pipelines with Scala/PySpark in AWS: 

• Describe the process of building data engineering pipelines using Scala/PySpark.

• Explain how AWS services like EKS (Elastic Kubernetes Service), Lambda, S3, Glue, and Step Functions are utilized within these pipelines.

• Provide examples of typical data engineering tasks such as data ingestion, transformation, and loading (ETL) processes that can be accomplished using these services.

2) Building Ingestion as a Service with AWS Native and PySpark:

• Discuss the significance of building ingestion as a service to facilitate the migration of data assets from on-premises to the cloud.

• Explain how AWS Native services and PySpark are leveraged to achieve this.

• Provide insights into the challenges and best practices associated with migrating data workloads to the cloud.

3) Implementing Test Data Driven Development Practices in Data Engineering:

• Define Test Data Driven Development (TDD) practices and their relevance in data engineering.

• Discuss how TDD principles are applied to the development of data engineering components.

• Highlight the benefits of adopting TDD, such as improved code quality, faster development cycles, and easier maintenance.

4) Setting up CI/CD for Data Engineering Workloads:

• Explain the importance of Continuous Integration/Continuous Deployment (CI/CD) in the context of data engineering.

• Describe the process of setting up CI/CD pipelines using AWS CloudFormation templates and Jenkins.

• Discuss how CI/CD pipelines streamline the deployment of data engineering solutions, ensure consistency, and reduce deployment errors.

5) Collaborating with Business and Technology Colleagues to Create Modern Platforms:

• Emphasize the importance of collaboration between business and technology teams in building data engineering platforms.

• Highlight the role of data engineers in understanding business requirements and translating them into technical solutions.

• Provide examples of modern, consumer-focused technologies that can be incorporated into data engineering platforms to enhance user experience and drive business value.

6) Utilizing SQL Skills for Data Profiling, Analysis, and Reverse Engineering:

• Discuss the significance of SQL skills in data engineering for tasks such as data profiling, analysis, and reverse engineering.

• Provide examples of SQL queries used for data profiling and analysis.

• Explain how data engineers can reverse engineer existing databases to understand their structure and relationships, enabling effective data migration and integration.

Make your plan to get a job by checking out some AWS Data Engineer Jobs Description:

1. Develop, build data engineering pipelines using Scala/PySpark in AWS using services like EKS, Lambda, S3, Glue, Step Functions

2. Build ingestion as a service using the AWS Native and PySpark to facilitate the data gravity shift from on-premises to the cloud through migration of data assets and workloads to the cloud

3. Build components in the data engineering following Test Data Driven development practices

4. Setup CICD for all data engineering workload using AWS Cloud formation templates and Jenkins process.

5. Work closely with business and technology colleagues to create platforms built using modern, consumer focused technologies

6. SQL skills for data profiling and analysis, and ability to reverse engineer existing databases with minimal assistance

7. Support data requests/projects and helps to interpret data sets

8. Perform ad-hoc data/process analysis as needed

9. Participate in Agile scrum teams and contribute to project planning and work prioritization.

CONCLUSION: 

In conclusion, becoming an AWS Data Engineer requires a blend of technical expertise, collaborative spirit, and a dedication to staying updated with the latest advancements in cloud technologies. By mastering tools like Scala/PySpark and AWS services such as EKS, Lambda, S3, Glue, and Step Functions, you'll be well-equipped to develop robust data engineering pipelines and facilitate seamless data migrations to the cloud. Remember, continuous learning and adaptability are key in this ever-evolving field. So, embark on your journey with confidence, knowing that you have the skills and knowledge to make a significant impact in the world of data engineering on AWS.