Role Description & Responsibilities
Data Engineering
About Databricks
Databricks is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Data Engineering role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly.
Key Responsibilities & Day-to-Day Scope
- ✓ Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems
- ✓ Own production rollouts of consumer and internally facing GenAI applications
- ✓ Serve as a trusted technical advisor to customers across a variety of domains
- ✓ Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally
- ✓ Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap
- ✓ Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy
- ✓ Expertise in deploying production-grade GenAI applications, including evaluation and optimizations
Official Qualifications & Technical Requirements
- • Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.
- • Experience building production-grade machine learning deployments on AWS, Azure, or GCP
- • Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
- • Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
- • Passion for collaboration, life-long learning, and driving business value through AI
- • [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets
- • Willing to travel once every 4-8 weeks to see customers (as needed)
Preferred Skills & Technology Stack
- + Experience with big data tools (Apache Spark, Kafka, Flink) and cloud platforms (AWS, Azure, GCP).
- + Familiarity with dbt (data build tool) for SQL transformations.
- + Hands-on experience with Docker and Linux system administration.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Databricks
Verified EmployerDatabricks champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.