Job Overview
We are looking for a highly technical and innovative Big Data Lecturer to guide students through the complexities of massive-scale data processing and distributed computing. You will be responsible for teaching the architecture, tools, and methodologies required to store, process, and analyze petabytes of information. Your role is to transform students into architects of the modern data ecosystem, capable of harnessing the power of big data to solve complex global challenges.

Job Responsibilities
Instructional Delivery: Lead lectures and intensive technical workshops on Big Data foundations, including Distributed Computing, NoSQL databases, Data Warehousing, Stream Processing, and Data Lake architectures.

Curriculum Development: Architect a cutting-edge curriculum that incorporates the latest industry standards in Big Data technologies (e.g., Apache Hadoop, Spark, Flink, Kafka, Cassandra, MongoDB, and Cloud-native Big Data services).

Engineering Mentorship: Oversee hands-on coding laboratories where students design and deploy end-to-end Big Data pipelines, focusing on performance, fault tolerance, and scalability.

Research & Scholarship: Engage in research related to distributed systems, scalable analytics, or data-intensive applications. Mentor graduate students in their capstone projects and thesis work.

Assessment & Evaluation: Create rigorous assessments, including cluster-design challenges, optimization projects, and performance-tuning tasks, ensuring students grasp both theoretical concepts and operational reality.

Integration of Best Practices: Teach the importance of data governance, security, and cost-efficiency in cloud-based Big Data environments.

Industry Collaboration: Maintain strong ties with the tech industry to ensure teaching materials reflect current production-grade Big Data engineering practices.

Job Requirements
Education: Master’s degree in Computer Science, Data Engineering, or a related field (a PhD is strongly preferred).

Experience: 3+ years of professional experience in Big Data engineering, cloud infrastructure, or data platform architecture, plus a demonstrated passion for teaching.

Technical Skills:

Distributed Systems: Deep understanding of distributed processing, consistency models, and fault-tolerant architectures.

Languages: Mastery of Python, Scala, or Java, with advanced SQL expertise.

Frameworks: Expert knowledge of the Apache ecosystem (Spark, Flink, Kafka, Hive).

Cloud Architecture: Hands-on proficiency with cloud-scale data platforms (AWS EMR/Glue, Google Cloud Dataflow/BigQuery, or Azure Synapse).

Soft Skills:

Translational Ability: Ability to break down the highly complex architectural trade-offs of Big Data into clear, conceptual lessons.

Troubleshooting Mindset: A proactive, investigative approach to debugging distributed systems and performance bottlenecks.

Intellectual Curiosity: A passion for staying at the cutting edge of a field that evolves rapidly.

Collaborative Leadership: Ability to manage collaborative student projects and foster a professional, engineering-focused classroom environment.

Benefits
Competitive Compensation: Attractive salary package with opportunities for research grants and consulting.

Infrastructure Access: Access to institutional high-performance computing (HPC) clusters and industry-standard cloud lab environments.

Professional Development: Support for publishing research, attending premier data conferences, and attaining advanced cloud certifications.

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

Career Growth: Clear paths to roles such as Tenured Professor, Big Data Lab Director, or Technical Advisor for data-intensive enterprises.

Big Data Lecturer, Data Engineering Faculty, Distributed Systems Instructor, Big Data Architect (Academic), Data Platform Specialist.

Industry Keywords: Hadoop, Spark, NoSQL, Data Lakes, Distributed Computing, ETL/ELT, Stream Processing, Data Governance, Cloud Architecture.

Attributes: Analytical, Rigorous, Systematic, Innovative, Technically-adept, Collaborative.

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