Data Engineer Syllabus
About The Training
In today’s data-driven economy, every successful product, analytic model, and strategic decision depends on clean, reliable, and accessible data. Data Engineers sit at the center of this process, building the frameworks that power products and innovation at scale.
This intensive, hands-on program addresses the real-world challenges faced by data teams. Guided by industry experts, participants work on the design, optimization, and automation of large-scale data systems, from building robust pipelines to architecting distributed data platforms.
Each stage builds in technical depth, introducing the trade-offs involved in balancing performance, reliability, and cost while preserving system flexibility. The track emphasizes engineering data solutions that operate at enterprise scale and enable advanced analytics and data-driven decision-making.
Stage 1: Software Engineering Foundations
The program begins by mastering the craft of building reliable, efficient, and scalable software- the backbone of every high-performing data system. Participants develop the mindset of systems engineers, gaining a deep understanding of how software interacts with infrastructure, performs under load, and remains maintainable over time.
Key skills and technologies:
- Python programming language
- PEP 8 coding standards
- Abstract Data Types
- Algorithms and Data structures
- Linux, Shell and Bash
- Structured programming
- Object-oriented programming
- Functional programming
- API development
- Interface design
- Complexity theory and practice
- Unit, regression and smoke testing
- Debugging techniques
- CI/CD principles
- Package release
- Dependency handling
- Deployment and containerization
- Industry-quality deliverables
Stage 2: Big Data Systems and Architecture
This stage transitions from individual components to system-level architecture. Participants explore how large datasets flow, transform, and scale across distributed environments, and how to engineer data pipelines that handle complex operational challenges.
Key skills and technologies:
- Data acquisition
- SQL, NoSQL
- DBMS
- Hadoop, HDFS and MapReduce
- Data pipelines
- ETL / ELT
- DAG design and Scheduling
- Batch processing
- Data storage
- Resource management
- Data scraping
- Data modeling
- Data governance and security
- File formats
- REST / RESTful API
- Asynchronous communication
- Process automation
- Orchestration and Synchronization
Stage 3: Systems Integration and Applied Data Engineering
In the final phase, participants design and implement a complete, production-grade data system. This stage integrates skills across software design and large-scale data architecture, and simulates the end-to-end ownership, reliability, and precision expected of professional data engineers.
Core focus areas include:
- Authentication and Access management
- Spark and PySpark
- ELK stack
- Data streaming (real-time) and integration
- Kafka
- Event-driven architectures
- Data ingestion
- Error handling
- Cloud tools for cost-efficient, resilient data solutions
- Data partitioning
- Scalable storage services (S3)
- Data lineage
- Data versioning
- Data observability
- dbt
- Data analysis
- Solution deployment
- Best practices, methodology and workflow