Senior Level Aws Data Engineer Jobs
Senior level aws data engineer jobs put experienced engineers in charge of platform architecture, data pipeline ownership, and the cross-functional initiatives that drive data strategy forward. Roles are concentrated across Technology & Software, Consulting & Professional Services, and Energy, with a mix of on-site, remote, and hybrid settings, and employers like EXL, ENGIE, and Capgemini hiring at this level now.
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Job Overview: We are seeking an experienced and visionary Senior Full-Stack Data Engineer to lead the architecture, development, and optimization of a next-generation data platform. This is a critical role for an individual with up to 15 years of deep data engineering expertise, capable of driving technical direction, mentoring team members, and delivering high-impact solutions in a fast-paced project environment.
Key Responsibilities:
- Data Pipeline Development & Management
- Ingestion & Transformation: Design, build, and optimize high-volume data ingestion and transformation jobs using tools like dbt Core, AWS Glue, ensuring data quality and integrity.
- Workflow Orchestration: Develop and maintain sophisticated data pipelines using orchestrators such as Dagster, focusing on modularity and reusability.
- Streaming & Real-time Integration: Implement and manage real-time data flows utilizing Confluent platforms or native AWS streaming services (e.g., Kinesis) for immediate data availability.
- Data Security and Privacy: Data Anonymization, Compliance with Regulations
- AWS Architect development for Data Pipeline
- Be well versed with DataOps and DevOps fundamentals
- Assist and drive the Data Ecosystem Management & Monitoring
- Has experience with containerization and orchestration, specifically AWS ECS/ EKS.
- Infrastructure as Code (IaC): Can help with development and maintenance of some cloud foundation using IaC tools to ensure immutable, repeatable, and scalable deployments
- Open Table Formats & Management: Implement and maintain the Iceberg open table format, utilizing tools for efficient schema evolution and data management.
- Compute Engine Optimization: Optimize query performance and cost efficiency across our primary compute engines: Snowflake, Amazon Redshift, and AWS Athena.
- Observability & Monitoring: Integrate comprehensive monitoring and observability into all pipelines using Splunk to ensure high availability, rapidly identify bottlenecks, and troubleshoot production issues.
Candidate Profile:
- 15+ Years of hands-on, progressive experience in Data Engineering, Data Architecture, or a closely related Full-Stack Data role
- Deep conceptual understanding of core data engineering principles, ETL/ELT patterns, and metadata management
- Proven track record of building and managing petabyte-scale data infrastructure in a cloud-native environment
- Insurance industry experience is mandatory.
Tools:
- Cloud Environment: AWS (S3, IAM, VPC, etc.)
- Experience with Talend, dbt Core, Iceberg, AWS Glue Catalog, Snowflake, Redshift, Athena, Splunk, AWS streaming services, Git
- Strong SQL, Pyspark, and Python
Job Overview: We are seeking an experienced and visionary Senior Full-Stack Data Engineer to lead the architecture, development, and optimization of a next-generation data platform. This is a critical role for an individual with up to 15 years of deep data engineering expertise, capable of driving technical direction, mentoring team members, and delivering high-impact solutions in a fast-paced project environment.
Key Responsibilities:
- Data Pipeline Development & Management
- Ingestion & Transformation: Design, build, and optimize high-volume data ingestion and transformation jobs using tools like dbt Core, AWS Glue, ensuring data quality and integrity.
- Workflow Orchestration: Develop and maintain sophisticated data pipelines using orchestrators such as Dagster, focusing on modularity and reusability.
- Streaming & Real-time Integration: Implement and manage real-time data flows utilizing Confluent platforms or native AWS streaming services (e.g., Kinesis) for immediate data availability.
- Data Security and Privacy: Data Anonymization, Compliance with Regulations
- AWS Architect development for Data Pipeline
- Be well versed with DataOps and DevOps fundamentals
- Assist and drive the Data Ecosystem Management & Monitoring
- Has experience with containerization and orchestration, specifically AWS ECS/ EKS.
- Infrastructure as Code (IaC): Can help with development and maintenance of some cloud foundation using IaC tools to ensure immutable, repeatable, and scalable deployments
- Open Table Formats & Management: Implement and maintain the Iceberg open table format, utilizing tools for efficient schema evolution and data management.
- Compute Engine Optimization: Optimize query performance and cost efficiency across our primary compute engines: Snowflake, Amazon Redshift, and AWS Athena.
- Observability & Monitoring: Integrate comprehensive monitoring and observability into all pipelines using Splunk to ensure high availability, rapidly identify bottlenecks, and troubleshoot production issues.
Candidate Profile:
- 15+ Years of hands-on, progressive experience in Data Engineering, Data Architecture, or a closely related Full-Stack Data role
- Deep conceptual understanding of core data engineering principles, ETL/ELT patterns, and metadata management
- Proven track record of building and managing petabyte-scale data infrastructure in a cloud-native environment
- Insurance industry experience is mandatory.
Tools:
- Cloud Environment: AWS (S3, IAM, VPC, etc.)
- Experience with Talend, dbt Core, Iceberg, AWS Glue Catalog, Snowflake, Redshift, Athena, Splunk, AWS streaming services, Git
- Strong SQL, Pyspark, and Python
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Senior Level Aws Data Engineer Jobs: Frequently Asked Questions
How do I get a senior level aws data engineer job?
At the senior level, employers look beyond technical execution to architectural judgment and cross-team influence. Candidates who stand out demonstrate ownership of end-to-end data systems, a history of designing scalable pipelines on AWS services like Redshift, Glue, and Lake Formation, and the ability to mentor junior engineers. Certifications like AWS Certified Data Analytics or Solutions Architect carry weight, but documented impact on production systems matters most.
Which companies hire senior level aws data engineers?
Companies hiring senior level aws data engineers right now include EXL, ENGIE, and Capgemini, based on current listings on Migrate Mate as of September 2026. Hiring at this level tends to come from large enterprises scaling their cloud data platforms, technology firms building internal analytics infrastructure, and consulting organizations delivering data solutions for enterprise clients.
Are there remote senior level aws data engineer jobs?
Yes, remote and hybrid options are widely available at the senior level, though some roles require on-site presence for regulated industries or infrastructure-critical teams. About 50% of senior level aws data engineer openings are remote or hybrid as of September 2026, reflecting strong demand for experienced engineers who can lead distributed data initiatives without proximity to a central office.
What makes a aws data engineer role senior level?
A senior level aws data engineer role is defined by scope and ownership rather than task execution. Senior engineers are expected to set the architecture direction for data platforms, make infrastructure decisions that affect multiple teams, and take accountability for system reliability and scalability. Mentoring less experienced engineers and collaborating with data scientists, product managers, and engineering leaders are standard expectations, not occasional responsibilities.
Which industries hire the most senior level aws data engineers?
Senior Level aws data engineer roles concentrate in Technology & Software, Consulting & Professional Services, and Energy, based on current listings on Migrate Mate as of September 2026. These sectors drive hiring at this level because they manage large, complex data ecosystems where experienced engineers are needed to own platform design, ensure data quality at scale, and align infrastructure decisions with broader business objectives.