Required Qualifications
Deep experience with distributed data systems, SQL, and orchestration tools.
Experience tuning high-throughput database infrastructure.
Knowledge of Google’s GECX is a plus.
Familiarity with chunking strategies and embedding models.
Skillset Requirements
8+ yrs of ETL & Data Modeling: Designing pipelines for structured/unstructured data, normalization, deduplication, and semantic consistency.
3+ yrs of Vector Databases: pgvector, Redis, Azure AI Search, hybrid search, and index optimization.
3+ yrs of Distributed Data Systems: Kafka, Spark, Flink, or similar event-driven architectures.
Data Governance: Zero-trust access, privacy controls, compliance, and auditability.
Real-time Embedding Updates: Event-driven refresh pipelines for RAG and agent memory systems.
Chunking & Embeddings: Semantic chunking, metadata tagging, and embedding model selection.
Search Infrastructure: BM25, hybrid search, inverted indexes, and ranking algorithms.
Performance Tuning: High-throughput read/write optimization.
Data Quality & Lineage: Validation, schema enforcement, and lineage tracking (e.g., Great Expectations, OpenLineage).

Data Engineer

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