Job Overview
We are looking for a practical, industry-savvy Data Engineer Instructor to join our faculty. In this role, you will be the bridge between raw data and actionable insight, teaching students how to design, build, and maintain the complex data pipelines and infrastructure that power modern analytics. Your focus will be on "engineering" the data ecosystem—ensuring that data is reliable, scalable, and accessible—and preparing students for the high-demand world of data platform architecture and big data management.

Job Responsibilities
Instructional Delivery: Lead comprehensive courses on Data Pipeline Design (ETL/ELT), Data Warehousing, Cloud Data Platforms, Database Administration (SQL/NoSQL), and Big Data Processing frameworks.

Curriculum Development: Build and refine a curriculum centered on real-world engineering standards, emphasizing modular code, automated testing, and scalable architecture.

Hands-on Lab Management: Oversee coding laboratories where students design and deploy end-to-end data pipelines using industry-standard tools like Apache Spark, Kafka, Airflow, and SQL/NoSQL databases.

Industry Standards & Best Practices: Teach the fundamental "DataOps" mindset: focus on version control (Git), infrastructure as code (Terraform), CI/CD pipelines, and data quality/governance.

Project Mentorship: Guide students through capstone projects that involve processing high-volume, heterogeneous data sets to solve real-world business challenges.

Assessment & Feedback: Create rigorous assessments, including coding assessments, system design challenges, and peer reviews, that mirror professional engineering requirements.

Professional Engagement: Stay active in the data engineering community to ensure course content reflects current trends in distributed systems and cloud-native architecture.

Job Requirements
Education: Bachelor’s degree in Computer Science, Software Engineering, or a related field (Master’s or relevant professional certifications preferred).

Experience: 3+ years of professional experience as a Data Engineer, Software Engineer, or Data Architect, plus a strong passion for teaching/mentoring.

Technical Skills:

Languages: Expert-level Python or Scala, and advanced SQL proficiency.

Tools/Frameworks: Deep experience with Apache Spark, Airflow, and distributed processing.

Platforms: Hands-on experience with cloud-native data stacks (AWS, GCP, or Azure) and data warehousing (Snowflake, BigQuery, or Redshift).

Architecture: Solid understanding of batch vs. streaming data architectures and database internals.

Soft Skills:

Clarity: Ability to take complex, multi-stage data architectures and explain the "why" and "how" simply.

Troubleshooting Mindset: A natural tendency to debug and solve technical problems, encouraging students to be self-sufficient investigators.

Rigorousness: A strong commitment to clean, maintainable, and robust code.

Collaboration: A desire to work alongside data scientists and analysts to demonstrate the vital role of the data engineer in the team.

Benefits
Competitive Compensation: Attractive salary package with opportunities for industry-sponsored research or consulting projects.

Access to Tech: Opportunity to use and teach on state-of-the-art cloud platforms and enterprise-grade data tools.

Intellectual Growth: A collaborative environment that encourages the study of system architecture and distributed computing.

Comprehensive Benefits: Full insurance coverage, academic leave, and retirement benefits in accordance with local labor laws.

Career Growth: Path toward Senior Instructor, Lead Data Architect, or Program Director of Data Infrastructure.

Data Engineer Instructor, Data Platform Lead, Data Engineering Lecturer, Big Data Systems Trainer, Data Pipeline Architect.

Industry Keywords: ETL/ELT, Data Warehousing, Distributed Systems, Cloud Computing, Apache Spark, Data Governance, DataOps, SQL, Scalability.

Attributes: Practical, Systematic, Collaborative, Troubleshooting-focused, Detail-oriented, Technical.

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