Data Engineer

Job Description The Data Engineer designs, builds, and maintains reliable data pipelines, models, dashboards, and cloud data infrastructure that transform diverse source data into trusted, reusable data products for analytics, research, AI, and business decision-making. This role applies SQL, Python, modern data engineering practices, visualization tools, and AI-assisted development to improve data quality, automate workflows, and deliver scalable data and AI capabilities through internal tools, APIs, and lightweight applications. The position requires strong technical, analytical, communication, and project management skills, with experience in relational databases, cloud platforms, data visualization, and applied AI tools. The ideal candidate will be based in or near Philadelphia, PA, Chicago, IL, or Rockville, MD.

Job Responsibilities - Design, build, and maintain scalable, well-tested data pipelines and ELT/ETL workflows using SQL, Python, and cloud data platforms (e.g., BigQuery, Snowflake) to automate data ingestion, cleansing, and transformation - Model and curate documented, reusable data products (transformations, semantic layers) that analytics, research, and AI systems can depend on

- Advanced working SQL knowledge and experience with relational databases, query authoring, and performance tuning, plus working familiarity with cloud data warehouses (e.g., BigQuery, Snowflake) - Strong programming skills in Python (including Pandas) and demonstrated experience building data pipelines and automation - Hands-on experience applying AI to real problems—prompting and integrating LLMs, using AI-assisted development tools (e.g., GitHub Copilot, Claude, Cursor), and applying ML or statistical methods—with sound judgment about where AI does and does not add value

- 2+ years’ experience building data pipelines and working with relational SQL databases - Proficiency with Python (including Pandas) for data engineering and automation - Demonstrated experience using AI tools to solve problems—e.g., integrating LLMs or LLM APIs, AI-assisted coding, or applying ML—in a professional or project setting

- Preferred graduate degree in Computer Science, Engineering, or Math - Preferred experience with modern application development on a recent tech stack (e.g., Python/FastAPI or Node.js on the back end; React/TypeScript on the front end); experience with enterprise stacks such as Java/Spring Boot is a plus - Preferred familiarity with modern data stack tooling (e.g., dbt, Airflow/Dagster) and DataOps practices

- Develop, construct, test, and maintain cloud data architectures and infrastructure, applying DataOps and DevOps practices such as version control, CI/CD, and infrastructure as code. - Advanced working SQL knowledge and experience with relational databases, query authoring, and performance tuning, plus working familiarity with cloud data warehouses (e.g., BigQuery, Snowflake) - Strong programming skills in Python (including Pandas) and demonstrated experience building data pipelines and automation

Qualifications - 2+ years’ experience building data pipelines and working with relational SQL databases - Proficiency with Python (including Pandas) for data engineering and automation

- Preferred familiarity with GCP cloud services (BigQuery, Dataform, GCE) - Preferred familiarity with AWS cloud services (EKS, EC2, RDS, S3, CloudFront, IAM, CloudWatch) - Preferred familiarity with infrastructure as code (Terraform), GitOps (ArgoCD), container orchestration (Docker/Kubernetes), and CI/CD pipelines (GitLab CI/CD, GitHub Actions)

- Preferred familiarity with modern data stack tooling (e.g., dbt, Airflow/Dagster) and DataOps practices - Preferred familiarity with GCP cloud services (BigQuery, Dataform, GCE) - Preferred familiarity with AWS cloud services (EKS, EC2, RDS, S3, CloudFront, IAM, CloudWatch)

- Hands-on experience applying AI to real problems—prompting and integrating LLMs, using AI-assisted development tools (e.g., GitHub Copilot, Claude, Cursor), and applying ML or statistical methods—with sound judgment about where AI does and does not add value - Familiarity with modern data engineering tooling and practices, such as orchestration (Airflow, Dagster, or similar), transformation frameworks (e.g., dbt), and batch and streaming processing - Application development capability on a modern stack: building APIs and services (e.g., SpringBoot and React.js) to deliver data and AI features to end users

- Preferred experience with modern application development on a recent tech stack (e.g., Python/FastAPI or Node.js on the back end; React/TypeScript on the front end); experience with enterprise stacks such as Java/Spring Boot is a plus - Preferred familiarity with modern data stack tooling (e.g., dbt, Airflow/Dagster) and DataOps practices - Preferred familiarity with GCP cloud services (BigQuery, Dataform, GCE)

- Demonstrated experience using AI tools to solve problems—e.g., integrating LLMs or LLM APIs, AI-assisted coding, or applying ML—in a professional or project setting - Familiarity with data visualization tools, particularly Power BI or equivalent (Tableau, Sigma, etc.) - Minimum undergraduate degree in Computer Science, Engineering, or Math (equivalent practical experience considered)

- Preferred familiarity with AWS cloud services (EKS, EC2, RDS, S3, CloudFront, IAM, CloudWatch) - Preferred familiarity with infrastructure as code (Terraform), GitOps (ArgoCD), container orchestration (Docker/Kubernetes), and CI/CD pipelines (GitLab CI/CD, GitHub Actions) About Aramark

- Model and curate documented, reusable data products (transformations, semantic layers) that analytics, research, and AI systems can depend on - Create and maintain data visualization dashboards and reports for internal and external use (e.g., Power BI) - Apply AI tools—including large language models (LLMs), AI-assisted coding, and machine learning techniques—to solve data problems, accelerate delivery, and improve data reliability, efficiency, and quality

- Application development capability on a modern stack: building APIs and services (e.g., SpringBoot and React.js) to deliver data and AI features to end users - Data visualization expertise with proven examples in Power BI or comparable tools - Experience performing root-cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement

- Deliver clear, actionable updates and insights to stakeholders, including Executive, Product, Data, and Design teams - Develop, test, and maintain lightweight applications, internal tools, APIs, and user-facing interfaces on a modern tech stack (e.g., SpringBoot and React.js) to deliver data and AI capabilities to business users - Develop, construct, test, and maintain cloud data architectures and infrastructure, applying DataOps and DevOps practices such as version control, CI/CD, and infrastructure as code.

- Familiarity with modern data engineering tooling and practices, such as orchestration (Airflow, Dagster, or similar), transformation frameworks (e.g., dbt), and batch and streaming processing - Application development capability on a modern stack: building APIs and services (e.g., SpringBoot and React.js) to deliver data and AI features to end users - Data visualization expertise with proven examples in Power BI or comparable tools