Data Scientist

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.

This program is built for IT professionals who:

• 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

What you get

• Full-time W2 salary from day one
• Health, dental & vision insurance
• Corporate housing & relocation covered
• 401(k) eligibility after one year
• Fortune 1000 client exposure
• Dedicated support team & mentorship
• Performance bonuses
• 25+ years of placement expertise behind you

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
• Week 9: Client readiness — interview prep, professional skills, and placement support

Required skills & qualifications

• 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

Why this opportunity stands out

• 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

• Microsoft · Google · Johnson & Johnson · Walmart · PayPal · T-Mobile · Capital One · Wells Fargo · Nike · Dell · CVS · Verizon · McDonald’s · Charles Schwab · Fannie Mae · Charter

Ready to apply?

Submit your resume and complete our AI-powered interview for fastest consideration. We review applications on a rolling basis — cohort spots are limited.