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Associate Data Engineer

CleanSlate Technology Group Austin, TX

Austin, TX

Today

Full time 3d Design Tools Amazon Web Services Artificial Intelligence

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Job Description

Austin, TX (onsite) Associate Data Engineer role supporting client data engineering work within CleanSlate Technology Group’s Data and AI Practice.

Responsibilities

Develop and maintain defined components of data pipelines and platform elements for analytics, machine learning, and AI workloads using established designs and guidance from experienced engineers.
Complete assigned data ingestion, transformation, and orchestration tasks using documented requirements, team review, and established patterns.
Support solution design and technical discussions by asking questions, documenting decisions, and completing follow-up tasks with guidance from 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 and ask questions to strengthen understanding of team practices.
Follow engineering standards, patterns, and best practices.
Pursue ongoing learning and development in data engineering skills.

Requirements

Foundational hands-on experience building data pipelines, processing data, or working with data platforms via 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 filtering, joining, aggregating, and transforming data in relational tables.
Foundational understanding of data integration and pipeline concepts, with exposure to one or more: Spark, Databricks, Snowflake, Redshift, Azure Fabric, AWS Glue, or Azure Data Factory (not required to know all).
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 from hands-on projects, labs, coursework, internships, or professional experience.
Ability to collaborate effectively and communicate technical concepts clearly.

Technologies

Python
Java
Scala
SQL
Spark
Databricks
Snowflake
Redshift
Azure Fabric
AWS Glue
Azure Data Factory
AWS
Azure

Team and Role Overview

Cross-functional, highly collaborative teams combining CSTG consultants with client stakeholders to address complex data challenges.
Values engineers who are curious and pragmatic, with an interest in data engineering plus complementary understanding of AI and machine learning.
Role focuses on completing defined development, testing, and documentation tasks with guidance from experienced engineers.
Contributes to building and maintaining data pipelines and platform components for analytics, machine learning, and AI use cases.
Emphasizes execution, learning, and growth through clearly scoped assignments and feedback.
As experience grows, strengthens ability to complete defined pipeline tasks, validate data, troubleshoot common issues, and explain work with increasing independence.

Preferred Qualifications

AWS or Azure certifications; willingness to pursue certifications encouraged.
Databricks or Snowflake certifications; willingness to pursue certifications encouraged.
Exposure to agentic tooling like AWS Quick, Claude Work, Claude, Copilot, or 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 requiring communication of technical information to others.

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