Data Engineer

You will architect ETL pipelines, manage vector databases, enforce governance, and build real-time data flows that continuously update embeddings and indexes. Your work ensures AI agents operate with fresh, trustworthy information.

What You Will Do

• 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).