Jr. Data Engineer

Aramark•Rockville, MD

4d•$53,000 - $60,000

About The Position

The Jr. Data Engineer will support the development of Data Vault 2.0 solutions within the Modern Data Platform. Working alongside senior engineers and architects, they will help build and maintain staging, Raw Vault, and Business Vault structures that serve as the foundation for enterprise reporting, analytics, semantic models, AI applications, and data products. Engineers in this role will analyze source system data, participate in source-to-target mapping activities, develop Data Vault 2.0 objects, validate data quality, and document technical solutions. They will use Snowflake, SQL, dbt, and approved platform tools to support metadata-driven development and AI-first engineering workflows. The engineer will participate in code reviews, testing, deployment activities, and production support efforts while gaining exposure to the full data lifecycle--from source system ingestion through Data Vault development and downstream consumption.

Requirements

Bachelor's degree in Computer Science, Information Systems, Information Technology, Data Analytics, Engineering, Mathematics, or a related technical field.
0-2 years of professional experience, internship experience, research experience, academic project experience, or equivalent hands-on technical experience.
Demonstrated interest in technology, data, software development, analytics, artificial intelligence, or related technical disciplines.
Strong analytical and problem-solving skills with the ability to investigate issues, identify patterns, and develop practical solutions.
Demonstrated initiative and intellectual curiosity. Candidates should be comfortable researching unfamiliar topics, learning new technologies, and moving work forward without requiring detailed instructions for every task.
Strong attention to detail and commitment to producing accurate, high-quality work.
Strong written and verbal communication skills.
Ability to work effectively in a collaborative team environment and incorporate feedback from technical leaders and peers.
Interest in modern data engineering, cloud technologies, AI-first development, and data platforms.
Foundational knowledge of SQL or relational database concepts and the ability to learn Snowflake, Data Vault 2.0, metadata management, and platform engineering practices.

Nice To Haves

Academic, internship, co-op, research, or personal project experience involving databases, software development, analytics, cloud technologies, or data platforms.
Exposure to SQL, Python, or other programming languages through coursework, projects, internships, or self-directed learning.
Experience using generative AI tools, coding assistants, or AI-powered development tools to support learning, research, troubleshooting, or software development.
Familiarity with data warehousing, ETL/ELT processing, data modeling, metadata management, cloud platforms, or related data engineering concepts.
Experience working on collaborative technical projects involving version control, shared deliverables, or team-based development practices.
Participation in hackathons, technical clubs, open-source projects, competitions, research programs, certifications, or other extracurricular technical activities.
Familiarity with Git, Azure DevOps, or similar source control and project management tools.

Responsibilities

Develop and maintain Data Vault 2.0 solutions across staging, Raw Vault, and Business Vault layers within the Modern Data Platform.
Analyze source system data and participate in source-to-target mapping activities to support accurate ingestion and modeling.
Build and maintain Data Vault objects, including Hubs, Links, Satellites, and Business Vault structures using approved architectural standards and development patterns.
Validate business keys, relationships, metadata classifications, and model outputs before deployment.
Use Snowflake, SQL, dbt, and approved engineering tools to support metadata-driven development and AI-first engineering workflows.
Create and maintain technical documentation, metadata, and comments that support governance, lineage, discoverability, and AI consumption.
Participate in code reviews, testing, deployment, and validation activities to ensure solutions meet engineering and architectural standards.
Support data quality, reconciliation, and troubleshooting efforts across the Data Vault environment.
Collaborate with engineers, architects, and product teams to deliver governed, reusable, AI-ready data assets.