Basic proficiency in a relevant language (e.g., Python, Java, or Scala)

Understanding data types, control structures, and functions

Introduction to version control using Git

Fundamentals of relational databases (e.g., MySQL, PostgreSQL)

Basic SQL: CRUD operations, simple queries, and joins

Introduction to NoSQL concepts using systems like MongoDB

Understanding data formats (CSV, JSON, XML)

Basic data cleaning and transformation using scripting

Introduction to simple data visualization (using tools like Excel or basic Python libraries)

Overview of ETL (Extract, Transform, Load) processes

Simple data ingestion methods (e.g., reading files and basic API calls)

MCQs: Testing foundational knowledge in programming, SQL, and basic data concepts

Coding Tasks: Simple scripts to read, transform, and output data

Practical Exercises: Writing basic SQL queries and performing simple data ingestion tasks

Proficiency in a chosen programming language for data tasks (e.g., Python or Java)

Working with libraries for data manipulation (e.g., Pandas, NumPy)

Understanding of scripting and automation techniques

Advanced SQL queries, including subqueries, indexing, and query optimization

Introduction to data modeling techniques for relational and NoSQL databases

Basic understanding of data warehousing concepts

Designing and implementing simple ETL processes

Familiarity with data integration tools and frameworks

Introduction to data cleaning, transformation, and loading at scale

Basics of distributed processing frameworks (e.g., Apache Hadoop or Apache Spark)

Overview of cloud data services (e.g., Amazon Redshift, Google BigQuery)

Introduction to orchestration tools like Apache Airflow for scheduling data pipelines

Basic containerization concepts using Docker

MCQs: Covering advanced SQL, data modeling, and ETL concepts

Coding Exercises: Building simple ETL pipelines and performing data transformations

Practical Tasks: Debugging and optimizing sample data workflows

Designing and building scalable ETL pipelines using tools like Apache Airflow or similar

Advanced data transformation and cleaning techniques

Error handling, logging, and monitoring of data processes

Proficiency with big data frameworks such as Apache Spark or Hadoop

Understanding of parallel processing and optimization of distributed computations

Designing and implementing data warehousing solutions (e.g., Amazon Redshift, Snowflake)

Integration between data lakes and data warehouses

Performance tuning for large-scale data queries

Introduction to streaming data platforms (e.g., Apache Kafka, Spark Streaming)

Building pipelines that support real-time analytics

Deploying data solutions on cloud platforms (AWS, GCP, Azure)

Leveraging cloud-native services for scalability and reliability

Project-Based Tasks: End-to-end development of an ETL pipeline that processes and integrates data from multiple sources

Coding Challenges: Building scalable data workflows and optimizing query performance

Debugging & Performance Exercises: Identifying and resolving bottlenecks in distributed processing systems

Designing enterprise-level data architectures, including hybrid data lakes and warehouses

Implementing modular and reusable pipeline components using microservices architecture

Mastery in performance tuning for distributed data processing frameworks (Apache Spark, Hadoop)

Implementing efficient data partitioning, sharding, and indexing strategies

Advanced proficiency in real-time data processing and analytics with Apache Kafka, Apache Flink, or similar tools

Building resilient streaming pipelines with fault-tolerance and low-latency characteristics

Deep expertise in cloud data platforms and leveraging managed services (e.g., AWS Glue, Google Cloud Dataflow)

Implementing Infrastructure as Code (IaC) for scalable deployments using Terraform or CloudFormation

Enforcing data governance policies, data quality standards, and regulatory compliance (e.g., GDPR, HIPAA)

Integrating robust data security practices throughout the data pipeline lifecycle

Architectural Design Exercises: Creating and presenting scalable, secure data architectures for complex use cases

Advanced Debugging & Optimization Tasks: Solving real-world performance and scalability challenges in distributed systems

Expert-Level Code Reviews: Conducting in-depth audits of production-level data pipelines

Publishing technical articles, whitepapers, and case studies on advanced data engineering practices

Speaking at industry conferences and leading professional communities

Setting technical standards and best practices for data engineering initiatives

Integrating emerging technologies such as machine learning, AI, and edge computing into data pipelines

Pioneering new approaches to data ingestion, storage, and real-time analytics

Research and development in novel data processing techniques and architectures

Defining long-term data strategies that align with business objectives and drive digital transformation

Architecting fault-tolerant, globally scalable data systems that support complex, data-driven applications

Leading initiatives in data privacy, security, and governance to meet evolving regulatory requirements

Implementing end-to-end data lineage, auditing, and quality control frameworks

Leading and mentoring large data engineering teams and cross-functional initiatives

Driving continuous improvement through innovative solutions and best practice sharing

Whiteboard Sessions: Presenting and defending complex, forward-thinking data architectures

Strategic Project Evaluations: Leading innovation projects that push the boundaries of current data engineering practices

Leadership Assessments: Reviewing contributions to the data engineering community, mentorship effectiveness, and strategic impact on the organization