AWS Data Engineer

Qode Page; United States; On-site; USD 140,000 - 180,000; Full time

Job summary

Qode Page is seeking an AWS Data Engineer to design, develop, and maintain scalable data pipelines on AWS, collaborating with analysts, stakeholders, data scientists and developers to ensure data quality and cost-effective storage.

You will leverage AWS native services, Snowflake, and Databricks to transform data, implement CI/CD, and ensure secure, compliant cloud processing for diverse data sets including healthcare standards (FHIR).

Qualifications

• 10+ years of data engineering experience using AWS native technologies.
• Proficiency with Snowflake for ETL and data processing.
• Experience with streaming and batch data pipelines.
• Familiarity with DataOps and CI/CD on AWS.
• Hands-on Databricks experience.
• Knowledge of Dataiku required.
• Graduate/Post-Graduate degree in Computer Science or related field.
• Experience with AWS Glue, EMR, Lambda, Redshift, S3.

Responsibilities

• Lead and support the delivery of data platform modernization projects.
• Design and develop scalable data pipelines on AWS.
• Migrate workflows from on-prem to AWS, ensuring data quality and consistency.
• Design automations to resolve data inconsistencies and quality issues.
• Perform system testing and validation for successful integration.
• Implement security and compliance controls in the cloud environment.
• Ensure data quality pre- and post-migration through validation checks.
• Collaborate with data architects and lead developers to document manual data movement workflows.

Skills

Data engineering

AWS cloud

Databricks

Snowflake

ETL pipelines

CI/CD on AWS

Dataiku

Education

Graduate/Post-Graduate degree in Computer Science or related field

Tools

AWS Glue

Python

Snowflake

S3

Redshift

EMR

Lambda

Power BI

Tableau

QuickSight

FHIR

Job description

Job Brief
As an AWS Data Engineer, your role will be to design, develop, and maintain scalable data pipelines on AWS. You will work closely with technical analysts, client stakeholders, data scientists, and other team members to ensure data quality and integrity while optimizing data storage solutions for performance and cost-efficiency. This role requires leveraging AWS native technologies and Databricks for data transformations and scalable data processing.

Responsibilities

• Lead and support the delivery of data platform modernization projects
• Design and develop robust and scalable data pipelines leveraging AWS native services
• Migrate workflows from on-premise to AWS cloud, ensuring data quality and consistency
• Design automations and integrations to resolve data inconsistencies and quality issues
• Perform system testing and validation to ensure successful integration and functionality
• Implement security and compliance controls in the cloud environment
• Ensure data quality pre- and post-migration through validation checks and addressing issues regarding completeness, consistency, and accuracy of data sets
• Collaborate with data architects and lead developers to identify and document manual data movement workflows and design automation strategies

Skills and Requirements

• 10+ years' experience with a core data engineering skillset leveraging AWS native technologies (AWS Glue, Python, Snowflake, S3, Redshift)
• Experience in the design and development of robust and scalable data pipelines leveraging AWS native services
• Proficiency in leveraging Snowflake for data transformations, optimization of ETL pipelines, and scalable data processing
• Experience with streaming and batch data pipeline/engineering architectures
• Familiarity with DataOps concepts and tooling for source control and setting up CI/CD pipelines on AWS
• Hands-on experience with Databricks and a willingness to grow capabilities
• Experience with data engineering and storage solutions (AWS Glue, EMR, Lambda, Redshift, S3)
• Strong problem-solving and analytical skills
• Knowledge of Dataiku is needed
• Graduate/Post-Graduate degree in Computer Science or a related field
• AWS S3 (data storage, export, recall)
• Data pipelines (batch & near-real-time)
• Integration with external systems (FHIR)
• Cloud-native analytics
• BI integrations: Power BI, Tableau, QuickSight