Data Engineer (Canadian Citizen or PR)

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).

Design and implement data pipelines for structured and unstructured data, focusing on semantic consistency and deduplication. Develop and optimize search infrastructure and real-time embedding updates for RAG and agent memory systems.

Requires expertise in distributed data systems, vector databases, and data governance. Candidates must be proficient in performance tuning, chunking strategies, and data lineage tracking.

• RedisSouhaitée

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• ETL
• Data Modeling
• Vector Databases
• Pgvector
• Redis
• Azure AI Search
• Kafka
• Spark
• Flink
• Data Governance
• RAG
• Semantic Chunking
• BM25
• Performance Tuning
• Data Quality
• Lineage Tracking

• Data & Analytics
• Technology
• Software
• Consulting
• Engineering

Expérience minimale

5+ ans

Postuler avant le

3 sept. 2026

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Niveau d’expérience

Mid-Senior level

Mode de candidature

La candidature directe est offerte