Data Engineer Level 2

1. 3+ years of applied data science experience, with demonstrated progression in scope and technical complexity
2. Hands-on experience with Generative AI applications, including one or more of: LLM fine-tuning, prompt engineering, RAG pipelines, or agentic workflow development
3. Familiarity with causal ML and/or causal inference methods (e.g., CATE, heterogeneous treatment effect modeling, DiD, matching)
4. Strong proficiency in Python, SQL, and Git
5. Experience with Azure and Databricks, or comparable cloud-based data science platforms
6. Experience contributing to production-quality ML systems using software engineering best practice
7. Ability to partner with product managers and stakeholders to translate business needs into science solutions and roadmap priorities
8. Strong oral and written communication skills, with the ability to translate between technical and business audiences
9. Comfort with ambiguity-able to operate effectively in evolving problem spaces and contribute to early-stage vision and strategy

Preferred:

1. Experience with MLOps practices including workflow orchestration, model monitoring, reproducibility, and deployment
2. Experience in retail, CPG, media, or marketplace analytics
3. Demonstrated ability to informally mentor or coach peers in technical best practices
4. Familiarity with experimentation frameworks and measurement pipelines