Job Type
Full-time
Description
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.
Requirements
- 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.
