AWS Data Engineer

Qode; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

Qode is seeking an experienced AWS Data Engineer to design, develop, and maintain scalable data pipelines on AWS. You will collaborate with analysts, stakeholders, data scientists, and engineers to ensure data quality and cost-efficient storage.

The role emphasizes building robust ETL, migrating on-prem workloads to the cloud, and implementing secure, compliant data handling using native AWS services and Databricks. Experience with data governance and multi-account architectures is valued.

Qualifications

• Graduate or postgraduate degree in Computer Science or related field.
• 10+ years of data engineering experience with AWS tech stack.
• Strong knowledge of data pipelines, ETL/ELT, and data quality.
• Experience with cloud data warehousing and analytics platforms.

Responsibilities

• Lead and deliver data platform modernization on AWS.
• Design and develop robust, scalable data pipelines using AWS native services.
• Optimize ETL processes for performance and cost efficiency.
• Migrate on-prem workloads to AWS with data quality controls.
• Design automation and integration to resolve data quality issues.
• Perform system testing and validation of data flows.
• Implement security and compliance controls in the cloud.
• Collaborate with architects and developers to document data workflows.
• Ensure data integrity pre- and post-migration.

Skills

AWS Glue

Python

Snowflake

S3

Redshift

Databricks

Dataiku

Education

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

Tools

EMR

Lambda

CI/CD on AWS

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.
• Optimize ETL processes, ensuring efficient data transformation.
• 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)
• Athena (querying data lakes)
• Data pipelines (batch & near-real-time)
• Integration with external systems (FHIR)
• Secure data handling (KMS, Macie)
• Cloud-native analytics
• Multi-account, multi-region data architecture
• BI integrations: Power BI, Tableau, QuickSight