Data Engineer

Stop waiting for the right opportunity. In 9 weeks, go from data-aware developer to a fully deployed Data Engineer - paid from day one, trained on real tools, and placed on Fortune 1000 client projects., You already write code. You work with data. You know SQL and Python aren't just buzzwords - they're tools you use daily. What you haven't had yet is the structured path to go from writing scripts and queries to engineering the data pipelines, warehouses, and big data systems that power real enterprise operations., * Apache Spark, Hadoop, Hive, Kafka & Airflow pipelines

• Snowflake, BigQuery, and modern cloud data warehousing
• ETL/ELT pipeline design, orchestration, and optimization
• Batch and real-time streaming data architectures
• Data modeling, schema design, and governance best practices
• Docker, Kubernetes, and containerized data workflows

Cloud platforms & AI tooling

• AWS, Azure, and GCP - data and ML services
• CI/CD pipelines and DevOps for data engineering
• NoSQL databases: MongoDB, Cassandra, and DynamoDB
• Data security, GDPR, HIPAA, and compliance requirements
• AI-assisted development with GitHub Copilot & Claude AI
• Agile, Scrum, and enterprise delivery workflows

Program timeline

Weeks 1-2

Python, SQL, and data engineering foundations - pipelines, APIs, and OOP at scale

Weeks 3-4

Big Data tooling - Spark, Kafka, Airflow, Hadoop, Hive, and cloud data platforms

Weeks 5-6

Cloud data warehousing - Snowflake, BigQuery, AWS/Azure/GCP data services

Weeks 7-8

Advanced pipelines, streaming architectures, DevOps, CI/CD, and data security

• Have 1+ year of professional coding experience - Python and SQL used regularly at work
• Understand data at a technical level - queries, schemas, pipelines, not just dashboards
• Know OOP concepts and can write structured, maintainable code
• Have exposure to cloud platforms or big data tooling - even at a basic level
• Are ready to go deep on AI/ML engineering, not just learn the buzzwords
• Are willing to relocate to Atlanta, GA for training and to client sites for project assignments, * Python: 1+ year of Python as a core part of your job - scripts, pipelines, automation, or data processing
• SQL: Proficient SQL - JOINs, aggregations, subqueries, and working with real databases professionally
• Data: Hands-on data wrangling experience - cleaning, transforming, and handling messy real-world datasets
• Git: Git version control in a team environment - branching, pull requests, and collaborative workflows
• APIs: REST API experience in Python - hitting endpoints, parsing JSON, handling authentication
• OOP: Solid understanding of OOP - classes, inheritance, interfaces used in real production code
• Stats: Basic statistics - distributions, mean/median/std dev, and understanding what data is telling you
• Location: Willingness to relocate to Atlanta, GA for training and travel to client sites for project placements

Highly preferred - fast-tracks your application

• Big data tooling exposure: Spark, Kafka, Airflow, Hadoop, Hive, or Flink
• Cloud platform experience: AWS, Azure, or GCP - especially data or storage services
• Data warehouse or BI tool experience: Snowflake, BigQuery, Tableau, or Power BI
• NoSQL database experience: MongoDB, Cassandra, DynamoDB, or similar
• DevOps basics: CI/CD pipelines, Docker, or containerized data environments
• Active GitHub profile with Python scripts, ETL projects, or data pipeline work
• Familiarity with data governance, GDPR, HIPAA, or security compliance requirements

Education

• Bachelor's degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or a related quantitative field
• Strong candidates from non-traditional backgrounds with demonstrable Python and data engineering experience are also considered, Strong English communication skills (written and verbal) are required for all client-facing roles.

Data engineering is one of the most in-demand specializations in tech right now - and most companies can't hire fast enough. Most developers spend years slowly picking up data engineering skills between projects, side reading, and online courses. This program compresses that into 9 weeks of structured, hands-on training across Python, SQL, big data tooling, and cloud platforms - then places you directly on paid engagements with Fortune 1000 clients across finance, healthcare, retail, logistics, and tech. You will leave with: * Real enterprise data pipeline and warehouse experience - not toy datasets * Hands-on big data and cloud engineering skills most engineers are still self-teaching * Verifiable Fortune 1000 consulting experience on your resume * A peer network of engineers who went through the same intensive program * A clear, supported pathway to senior-level Data Engineer placement Our clients include