CleanSlate Technology Group (CSTG) is a modern technology consulting firm transforming how organizations use Data, AI, and Cloud Modernization to fuel innovation. As a top-tier cloud partner, we design and deliver AI solutions and modern data platforms that unlock the power of data, accelerate transformation, and enable businesses to build what's next.  We help clients accelerate their AI journey with security and governance built on a strong data foundation. With an operational mindset, we break through legacy limitations, simplify complexity, and create future-ready technology foundations that inspire new possibilities. At CSTG, you'll be part of a team that empowers organizations to innovate faster, make smarter decisions, and compete boldly in a rapidly evolving digital world.   

Through our Data and AI Practice, we partner with clients to build scalable, reliable data foundations across AWS and Azure. Our focus is on modern data engineering—developing data platforms and pipelines that enable analytics, machine learning, and AI in real-world production environments. We believe strong data engineering is critical to making AI useful, trustworthy, and sustainable. 

CSTG consultants combine proven engineering practices with a hands-on consulting mindset, helping clients move from strategy to implementation while delivering solutions aligned to real business needs. 

What does your team look like: 

Our teams are highly collaborative and cross-functional, blending CSTG consultants with client stakeholders to solve complex data challenges. We value engineers who are curious and pragmatic, and who want to “major” in data engineering with a complementary understanding of AI and machine learning to build durable, production-ready data solutions. 

What does your role look like: 

As an Associate Data Engineer in CSTG’s Data and AI Practice, you will contribute to client data engineering projects by completing defined development, testing, and documentation tasks with guidance from experienced engineers. You will help build and maintain data pipelines and platform components that support analytics, machine learning, and AI use cases. 

This role emphasizes execution, learning, and growth through clearly scoped assignments, established engineering patterns, and feedback from more experienced team members. You will participate in solution discussions to build your understanding of design decisions and join client conversations with support from the delivery team, communicating progress, asking questions, and raising issues clearly and professionally. 

As you gain experience across client engagements and problem domains, you will strengthen your ability to complete defined pipeline tasks, validate data, troubleshoot common issues, and explain your work, taking on greater complexity and independence as your skills develop. 

Responsibilities: 

• Develop and maintain defined components of data pipelines and platforms supporting analytics, machine learning, and AI workloads, using established designs and guidance from experienced engineers. 
• Implement assigned data ingestion, transformation, and orchestration tasks using established patterns, documented requirements, and team review. 
• Participate in solution design and technical discussions by asking questions, documenting decisions, and completing follow-up tasks with guidance from more senior team members. 
• Write clean, maintainable, and well-documented code aligned with team standards. 
• Execute assigned tests and data validation checks, document results, and assist with deployment, monitoring, and troubleshooting of production data solutions under team guidance; escalate unexpected results and issues to the appropriate team member. 
• Collaborate with CSTG team members and client stakeholders by sharing task updates, documenting questions and requirements, and promptly raising blockers with the delivery team. 
• Participate in code reviews, design discussions, and agile team ceremonies; incorporate feedback into your work and ask questions to strengthen your understanding of team practices. 
• Follow and apply engineering standards, patterns, and best practices. 
• Pursue ongoing learning and development of data engineering skills. 

• Foundational hands-on experience building data pipelines, processing data, or working with data platforms through employment, internships, apprenticeships, coursework, or personal projects. 
• Foundational programming skills in Python, Java, or Scala, demonstrated through scripts or projects that process, transform, or move data. 
• Working knowledge of SQL, including the ability to filter, join, aggregate, and transform data in relational tables. 
• Foundational understanding of data integration and pipeline concepts, with exposure to one or more relevant technologies, such as Spark, Databricks, Snowflake, Redshift, Azure Fabric, AWS Glue, or Azure Data Factory; familiarity with every listed technology is not required. 
• Familiarity with data modeling concepts and analytics-focused data structures. 
• Understanding of data quality concepts such as validation and consistency 
• Foundational familiarity with AWS and/or Azure data services, gained through hands-on projects, labs, coursework, internships, or professional experience. 
• Ability to collaborate effectively within a team and communicate technical concepts clearly. 

Preferred Qualifications: 

• AWS or Azure certifications; willingness to pursue certifications encouraged. 
• Databricks or Snowflake certifications; willingness to pursue certifications encouraged. 
• Agentic tooling like AWS Quick/Claude Work/Claude/Copilot/OpenClaw.  
• Exposure to CI/CD pipelines or infrastructure-as-code concepts through projects, coursework, or professional experience. 
• Exposure to NoSQL databases or distributed data systems through coursework, labs, projects, or professional experience. 
• Exposure to AI-powered software development tools (such as Claude Code, Cursor, Kiro, or similar). 
• Exposure to consulting, client-facing work, or collaborative projects that required communicating technical information to others.