Data Engineer

About the role

This program compresses years of data engineering skills 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 various industries.

Responsibilities

• Develop and optimize data pipelines using Apache Spark, Hadoop, Hive, Kafka, and Airflow
• Design and implement data warehousing solutions with Snowflake, BigQuery, and modern cloud data platforms
• Build and manage real-time streaming data architectures
• Collaborate with cross-functional teams to design and implement data models, schemas, and governance strategies
• Implement and maintain containerized data workflows using Docker, Kubernetes, and cloud platforms
• Work with cloud platforms such as AWS, Azure, and GCP, including data and machine learning services
• Develop and maintain CI/CD pipelines for data engineering projects
• Ensure data security and compliance with GDPR, HIPAA, and other relevant regulations
• Assist with Agile and Scrum methodologies for data engineering projects

Requirements

• 1+ year of professional coding experience — Python and SQL used regularly at work
• Proficient in data at a technical level — queries, pipelines, not just dashboards
• Experience with object-oriented programming (OOP)
• Exposure to cloud platforms or big data tooling
• Willingness to relocate to Atlanta, GA for training and to client sites for project assignments

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
• 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

Skills

• Python
• SQL
• Data engineering
• Big data tooling
• Cloud platforms
• Data warehousing
• DevOps
• NoSQL databases
• GitHub

Benefits

• 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

Pay

Full-time W2 salary from day one

Schedule

Full-time employment