About the role
Requirements:
- At least 5-8 years of analytics engineering / data engineering, with a minimum of 2 years of experience in architecting and solutioning.
- Demonstrated track record of applying deep technical expertise to diagnose business problems rationally and solve them relentlessly. Note that we're NOT an AI lab doing cutting-edge research; this role demands strategizing, application, and execution.
- AI/LLM Readiness: Experience working with Vector Databases, Knowledge Graphs, or creating data layers specifically for Generative AI consumption.
- Data Modeling: Strong proficiency in dimensional modeling (Star Schema, Snowflake) and modern data lakehouse table formats (Delta Lake, Iceberg, or Hudi).
- Tech-Stack Adoption: Strong command of distributed computing principles. Spark, Hive, BigQuery. Understand DAGs, shuffling, serialization, and partition pruning.
- Data Engineering: Strong data engineering experience, implementing large-scale and complex systems.
- Deep Tuning Experience: Proven ability to look at a "spill-to-disk" error or a slow stage and know exactly which configuration knob to turn.
- Programming: Proficiency in Advanced SQL, Python, R, Spark, and Spark SQL. Good to have Scala.
- Familiarity with modern table formats (Delta Lake, Iceberg, Hudi).
The "AI/Modern" Experience:
- Experience building data layers specifically for Generative AI (Vector DBs, RAG architectures, or Semantic Layers).
- Vibe coding an analytics UI/Dashboard using Lovable/Claude/etc. for POCs, which can then be replicated for implementation
- Prepare data for RAG (Retrieval-Augmented Generation) architectures.
- Nice to have: BI/metric layer tools (LookML/Transform/MetricFlow/Semantic Layer tools).