Staff Data Engineer

• Staff Data Engineer
• Location: Remote USA
• Pay: $200k - $240k Base

Overview

Join a fast-growing digital consultancy that partners with leading enterprises to solve complex technology challenges through modern cloud, data, and AI solutions. This confidential opportunity is ideal for a Staff Data Engineer who thrives in a highly collaborative environment, enjoys influencing technical strategy, and is passionate about building scalable AWS-based data platforms that enable next-generation AI capabilities.

The ideal candidate is a hands-on technical leader with deep AWS expertise and experience supporting AI/ML workloads from a data engineering perspective.

Responsibilities

• Design, build, and optimize scalable cloud-based data platforms and pipelines.
• Lead the architecture and implementation of modern data solutions on AWS.
• Develop and maintain reliable ETL/ELT processes for large-scale datasets.
• Partner with Data Science, AI/ML, Product, and Engineering teams to support advanced analytics and AI initiatives.
• Establish best practices for data quality, governance, observability, and performance.
• Drive technical strategy and provide mentorship to other data engineers.
• Improve data accessibility and self-service capabilities across the organization.
• Evaluate and implement emerging technologies to enhance platform capabilities.

• Must Have Qualifications
• 7+ years of experience in Data Engineering or related data platform roles.
• Expert-level experience with AWS cloud services and data ecosystem technologies.
• Experience supporting AI/ML initiatives from a Data Engineering perspective, including data preparation, feature pipelines, and model enablement.
• Expertise with SQL and Python.
• Bachelor's degree in Computer Science, Information Systems, or a related field.

• Preferred Qualifications
• Experience with AWS Bedrock.
• Experience with Amazon SageMaker.
• Experience with modern data lake, lakehouse, or analytics architectures.
• Experience leading technical initiatives in large-scale cloud environments.