Data Engineer | Onsite | Canada | CAD Salary

HR Ways; Brampton; On-site; CAD 90,000 - 130,000; Full time

Job summary

HR Ways seeks a Data Engineer for a global IT and consulting client. You will design and operate data pipelines, vector databases, and governance within AI contexts, delivering real-time embedding updates and scalable architectures.

Based in Brampton, Ontario, work hours 9am–6pm, Monday–Friday, onsite. 5–7+ years of experience in distributed data systems and SQL are required, with familiarity in Kafka, Spark, and embedding models.

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.

Responsibilities

• 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.

Skills

Distributed data systems

DB performance tuning

GECX knowledge

Chunking & embeddings

ETL & Data Modeling

Vector databases

Distributed data frameworks

Data governance

Real-time embeddings

Search infrastructure

Performance tuning

Data quality & lineage

Tools

Kafka

Spark

Flink

Job description

About client:

A global IT and consulting company based in Bangalore, India providing IT services to clients worldwide across multiple industries.

Opening: Data Engineer

Job Description

Responsibilities:

• 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.

Experience Required:

5 years to 7+ years

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).

Other Details:

Experience: 5-7 years

Work Timings: 9am-6pm

Work Days: Monday- Friday

Work Mode: Onsite

Location: Brampton, Ontario, Canada