Sr. AWS Data Engineer / Architect (AWS Lakehouse / CDK / CI-CD)

Role: Sr. AWS Data Engineer / Architect (AWS Lakehouse / CDK / CI-CD)

Duration: 12+ Month Contract-to-Hire

Location: Remote

Important Note:

• who can build the lakehouse with the use AI
  
• Hands-on building out in CDK
  
• There are components we don’t have today with DQ, metadata management, security with AWS lake formation, etc. that ideally someone else has established before.  Someone who knows how to use AI as an accelerator in building out all these pipelines so we can do more with less.
  

Data Engineer (AWS Lakehouse / CDK / CI/CD)

We are seeking a high-caliber, hands-on AWS Data Engineer to design, validate, and build production-grade data pipelines and a modern lakehouse platform. This role is both execution-focused and architecture-partnering, ideal for someone who can independently build complex AWS data solutions while also helping validate and refine the target architecture alongside senior technical leadership.

This is a developer-first engineering role, not a platform-only or governance-only position. We are looking for someone who has deep AWS data engineering experience and has likely already operated at a senior or principal-level capacity in building cloud data platforms (similar to engineers who have built end-to-end AWS lakehouse environments in production).

What You’ll Do

• Design, build, and deploy production data pipelines on AWS
• Build batch and event-driven ingestion pipelines using:
• AWS Glue (PySpark) / Spark on EMR
• Lambda, EventBridge, SQS, Kinesis
• Develop and optimize S3-based lakehouse architectures
• Build transformation layers using dbt Core, including testing and validation frameworks
• Implement infrastructure using AWS CDK (preferred), CloudFormation, or Terraform
• Own deployment processes through CI/CD pipelines (GitHub Actions, CodePipeline, etc.)
• Collaborate closely with architecture leadership to:
• Validate and refine lakehouse architecture decisions
• Identify gaps in current design (data quality, metadata, security, governance)
• Help translate architecture into production-ready implementation
• Contribute to data quality (DQ), metadata management, and governance patterns, leveraging AWS-native services (e.g., Glue Catalog, Lake Formation)
• Use AI tools as a development accelerator to improve pipeline delivery speed, testing, and automation

Must-Have Experience

• Proven experience as a hands-on AWS Data Engineer building production-scale data platforms
• Experience building or significantly contributing to end-to-end AWS lakehouse architectures
• Strong hands-on development with:
• AWS Glue (PySpark) or Spark on EMR
• Experience building event-driven architectures using AWS services:
• Lambda, EventBridge, SQS, Kinesis
• Strong experience with S3-based data lake or lakehouse architectures
• Deep proficiency in SQL and dbt Core (modeling, transformations, testing)
• Strong experience with Infrastructure as Code:
• AWS CDK (strong preference), CloudFormation, or Terraform
• Experience building and deploying through CI/CD pipelines
• Demonstrated ability to operate across both:
• Architecture validation / design discussions
• Hands-on implementation and delivery
• Strong AWS-native engineering mindset (able to leverage managed services effectively without over-reliance on heavy external platforms)

Nice to Have

• AWS Certifications:
• AWS Certified Data Analytics – Specialty
• AWS Certified Developer – Associate
• Databricks Certified Data Engineer Associate
• Experience implementing or working with:
• Data quality frameworks and observability patterns
• Metadata management solutions
• AWS Lake Formation (security/governance)
• Exposure to MDM concepts or implementations (vendor still being evaluated; not required to be expert)
• Experience using AI-assisted engineering tools to accelerate development and delivery
• Experience helping define or evolve modern AWS lakehouse architecture standards

Environment Context

We are actively building a modern AWS lakehouse platform that includes:

• A S3-based lakehouse architecture
• A combination of:
• Batch pipelines
• Event-driven ingestion pipelines
• Infrastructure managed through CDK and CI/CD pipelines
• Emerging capabilities in:
• Data quality frameworks
• Metadata management
• Security and governance (including AWS Lake Formation)

We are building this in close partnership with senior technical leadership and are actively validating architectural decisions as we implement.

This role is best suited for someone who has likely already built or heavily contributed to a production AWS data platform of similar complexity, and can both:

• Help ensure we are not missing critical architectural components
• And immediately contribute hands-on to building and scaling the system

What Success Looks Like

• Production-grade AWS pipelines deployed reliably via CI/CD
• Clean, testable dbt models powering analytics and downstream systems
• Event-driven and batch pipelines operating cohesively in a unified lakehouse architecture
• Infrastructure fully codified in CDK or equivalent IaC
• Tangible improvements in data quality, observability, and governance maturity
• Architecture decisions validated through implementation, not just design discussion
• Increased engineering velocity through effective use of AI-assisted development

Thanks 

John

Netstrive Consulting, LLC

450, E. 22^nd St, Suite 178,

Lombard, IL 60148

Direct:

Desk No: Ext-404

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