Associate Data Engineer

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.