Jr. Big Data Developer

Who this is for

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.

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

Required skills & qualifications

We review every resume carefully. Here is exactly what we look for — the more clearly your resume demonstrates these skills, the faster you will move through our process.

Must-have — non-negotiable

• 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