🕒 4 days ago
Build customer-facing data products for the single-family home market, architect reliable data models and workflows, and shape the data platform’s architecture and engineering standards.
AWS Data Engineer
📅 Jan 30
AWS GluePythonSnowflakeAmazon S3
📜 Description
- Lead and support data platform modernization projects.
- Design and develop scalable data pipelines using AWS services.
- Optimize ETL processes and data transformations.
- Migrate on-premise workflows to AWS while maintaining data quality and consistency.
- Automate integrations and resolve data inconsistencies and quality issues.
- Test and validate systems, implement cloud security controls, and collaborate on workflow automation strategies.
🛠️ 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.
Full 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
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