Data Engineer
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Data Pipelines: Build ETL flows for structured/unstructured data, ensuring normalization, deduplication, and semantic consistency. Vector Infrastructure: Manage pgvector, Azure AI Search, Redis vector indexing, and hybrid search layers. Data Governance: Implement zero-trust access, privacy controls, and compliance within AI context pipelines. Real-time Processing: Build event-driven architectures that continuously refresh embeddings and indexes. 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
ETL & Data Modeling: Designing pipelines for structured/unstructured data, normalization, deduplication, and semantic consistency. Vector Databases: pgvector, Redis, Azure AI Search, hybrid search, and index optimization. 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).
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