Data Engineering Summer/Fall Co-Op (June - Dec '27)

Description

Data Engineering Intern

Responsibilities

What You Will Work On
• Develop and maintain data ingestion and transformation pipelines using Python, SQL, PySpark, HTML, JavaScript, and Databricks notebooks.
• Use Databricks Workflows and jobs to orchestrate, schedule, monitor, and troubleshoot data processing activities.
• Build and test ETL/ELT solutions for structured and semi-structured data from enterprise systems, databases, files, and APIs.
• Work with Delta Lake and lakehouse concepts, including Bronze, Silver, and Gold data layers.
• Apply data profiling, validation, reconciliation, and quality checks to improve data reliability.
• Assist with onboarding new datasets into Azure Data Lake Storage and Databricks.
• Support pipeline monitoring, root-cause analysis, defect resolution, and documentation.
• Use Git and CI/CD practices for version control, peer review, testing, and controlled deployments.
• Collaborate with data engineers, analysts, platform teams, and business stakeholders to understand requirements and deliver usable data products.

Databricks Learning Focus
Hands-on exposure may include: Databricks workspace and notebooks, Apache Spark and PySpark, Delta Lake, Databricks Workflows, SQL Warehouses, Unity Catalog fundamentals, data quality controls, performance basics, and lakehouse architecture.

What You Will Learn:

Databricks & Spark - Develop notebooks and scalable transformations with SQL, Python, PySpark, HTML, and JavaScript.
Pipeline Engineering - Understand ingestion, orchestration, testing, monitoring, and operational support.
Cloud Data Platforms - Work with Azure-based storage, integration, and data processing patterns.
Data Quality & Governance - Apply validation, documentation, access control, lineage, and reliability practices.
Engineering Delivery - Gain experience with Git, code reviews, CI/CD, Agile delivery, and stakeholder collaboration.

Ideal Candidate Profile
• Takes ownership of assigned work and communicates progress or blockers early.
• Approaches problems methodically and validates results before considering work complete.
• Can learn independently while seeking guidance at the right time.
• Values clean code, documentation, data security, and reliable delivery.
• Is interested in building a long-term career in data engineering and cloud data platforms.

Suggested Academic or Personal Projects
• An end-to-end ETL pipeline that ingests, cleans, transforms, and publishes a dataset.
• A Databricks or Spark project using Delta tables and Bronze, Silver, and Gold layers.
• A data quality or reconciliation framework using Python and SQL.
• A cloud-based analytics project with Azure storage, orchestration, and reporting.
• A database design, API ingestion, or data visualization project with clear documentation.

Required Experience and Skills

• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Electronics Engineering, or a related technical discipline.
• Must have the ability to work on-site 5 days per week
• Basic programming proficiency in Python, with familiarity in HTML and JavaScript, and the ability to write clear, testable code.
• Working knowledge of SQL, relational databases, joins, aggregations, and data manipulation.
• Understanding of data structures, algorithms, and software engineering fundamentals.
• Strong analytical and problem-solving skills with attention to detail.
• Clear written and verbal communication skills, with the ability to collaborate in a team environment.
• Curiosity, accountability, and willingness to learn new technologies.

Desired Experience and Skills

• Academic, internship, or personal project experience with Databricks, Apache Spark, or PySpark.
• Exposure to Microsoft Azure, Azure Data Factory, Azure Data Lake Storage, or Azure SQL.
• Understanding of ETL/ELT, data warehousing, lakehouse, or medallion architecture concepts.
• Familiarity with HTML, JavaScript, Git, Azure DevOps, CI/CD, Linux, REST APIs, or Power BI.
• Awareness of data governance, security, access control, or data quality principles.
• Databricks or Microsoft Azure learning credentials are an advantage but not required.