Master Python for data tasks and develop deep proficiency in SQL for data querying and transformation.

Understand essential data structures, algorithms, operating systems, and networking concepts.

Gain proficiency in version control with Git and containerization with Docker for reproducible environments.

Learn the principles and use cases for relational (PostgreSQL) and NoSQL (MongoDB, DynamoDB) databases.

Master concepts of modern cloud data warehouses like Snowflake, BigQuery, and Redshift for analytics.

Understand techniques for designing effective data schemas, including dimensional modeling (star/snowflake).

Gain deep expertise in Apache Spark for large-scale distributed data processing and analytics.

Learn to build real-time data pipelines using technologies like Apache Kafka, Flink, and Spark Streaming.

Understand Data Lakes (S3/GCS) and the modern Lakehouse architecture (Delta Lake, Iceberg, Hudi).

Master tools like Apache Airflow or Dagster to schedule, monitor, and manage complex data workflows.

Learn to design, build, and optimize robust and scalable ETL (Extract, Transform, Load) and ELT pipelines.

Implement frameworks like Great Expectations and dbt tests to ensure data accuracy and reliability.

Gain hands-on experience with the data services of major cloud providers (AWS, GCP, Azure).

Use Terraform to define and manage your data infrastructure programmatically for consistency and scalability.

Apply DevOps principles to data pipelines, creating automated CI/CD workflows for testing and deployment.

Understand principles of data security, privacy (GDPR, CCPA), access control, and data cataloging tools.

Learn to build the infrastructure and feature stores required to support Machine Learning engineers and Data Scientists.

Stay current with emerging architectural patterns like the Data Mesh and understand their implications for the enterprise.