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., Outstanding Expectations, OpenLineage).

📌 Data Engineer (Brampton)
🏢 Ampstek
📍 Brampton