Data Engineer

Develop core Python skills used in data engineering, including working with data, control flow, and notebooks.

Build reusable Python programs by working with dictionaries, functions, and Jupyter Notebook to support data engineering and analysis workflows.

Extend your Python skills for data engineering by working with real datasets, text processing, and object-oriented programming.

Develop a practical understanding of how Python represents data, encodes text, and works with files to optimize memory and disk usage.

Evaluate algorithm time and space complexity in Python, trade memory for speed, and design efficient solutions for data engineering workflows.

Learn to navigate the filesystem, manage permissions, and run scripts from the command line to support efficient, repeatable data workflows.

Learn to inspect files, read documentation, and process text efficiently using streams, redirection, and pipelines in real data workflows.

Strengthen your data analysis workflow with intermediate command line skills like piping, redirection, and transforming data directly from the shell.

Practice version control with Git to track changes, collaborate via GitHub, and manage real projects using workflows teams rely on every day.

Develop core SQL skills by writing queries to access, explore, and manipulate data stored in relational databases for common data analysis tasks.

Summarize large datasets by computing statistics, grouping records, and applying SQL aggregate functions to extract meaningful insights.

Combine and analyze data across multiple tables by applying SQL joins and set operators to produce comprehensive, query-ready datasets.

Query SQLite databases from Python by executing SQL statements and working with cursors to retrieve and analyze data.

Write scalable, advanced SQL queries by nesting subqueries and using common table expressions to solve complex analysis problems.

Build hands-on PostgreSQL skills for data engineering by designing tables, loading CSV data, and managing databases beyond SQLite.

Optimize PostgreSQL performance by diagnosing slow queries, using EXPLAIN, indexing tables, and applying core database internals in practice.

Move beyond traditional SQL by working with Snowflake and NoSQL databases to design scalable, production-ready data systems.

Apply NumPy array operations to process large datasets efficiently, perform fast numerical computations, and optimize Python workflows for data engineering.

Optimize pandas workflows to handle larger datasets by reducing memory usage, processing data in chunks, and combining pandas with SQLite.

Scale data processing workflows by applying parallel processing and MapReduce techniques to efficiently analyze large datasets.

Build core data structures such as linked lists, stacks, queues, and dictionaries to write more efficient and scalable programs.

Explore recursion, binary trees, binary heaps, and more with ready-to-use tactics for real projects.

Work with Apache Spark to process massive datasets using RDDs, DataFrames, and Spark SQL across distributed environments.

Move beyond notebooks to build production-grade PySpark ETL pipelines that handle messy data, scale efficiently, and run reliably in the cloud.

Create reproducible data engineering environments with Docker, ensuring pipelines run the same across machines and teams.

Orchestrate containerized applications with Kubernetes, automating deployment, scaling, networking, and resilience for production systems.

Build a practical Python data pipeline using imperative and functional patterns, including scheduling, decorators, and real-world workflows.

Outgrow fragile scripts and cron jobs by orchestrating reliable, production-ready data pipelines with Apache Airflow.

Understand cloud computing fundamentals including service models, deployment strategies, and how major cloud providers compare.

Deploy production Apache Airflow pipelines to both AWS and GCP using Docker, managed container services, and cloud storage.