We are seeking an experienced Data Engineer with 5–7 years of experience in developing scalable ETL/ELT processes, data pipelines, and real-time data processing solutions. The ideal candidate will have hands‑on experience with vector databases and search technologies, including pgVector, Azure AI Search, and Redis, along with a strong understanding of data governance and compliance.
Key Responsibilities Design, develop, and maintain scalable ETL/ELT data pipelines.
Build batch and real-time data processing solutions for structured and unstructured data.
Develop data ingestion, transformation, validation, and integration workflows.
Work with pgVector, Azure AI Search, Redis, and other vector search technologies.
Support data pipelines for AI/ML, Generative AI, RAG, embeddings, and semantic search use cases.
Optimize pipelines for performance, scalability, reliability, and data quality.
Implement monitoring, validation, error handling, and recovery mechanisms.
Work with relational and NoSQL databases.
Implement data governance, security, privacy, compliance, and access controls.
Maintain data lineage, metadata, documentation, and auditability.
Collaborate with Data Scientists, Software Engineers, Architects, and business stakeholders.




Troubleshoot pipeline failures and resolve data-quality and performance issues.
Required Skills 5–7 years of experience as a Data Engineer.
Solid hands‑on experience with ETL/ELT and data pipeline development.
Experience with real‑time/streaming data processing.
Hands‑on experience with one or more of: pgVector
Redis

Strong Python and SQL skills.
Experience with data modeling and database technologies.
Experience working with structured and unstructured data.
Knowledge of data governance, data quality, security, and compliance.
Experience with cloud data platforms, preferably Microsoft Azure.
Experience with APIs and data integration.
Familiarity with Git and CI/CD practices.
Preferred Skills Experience with Generative AI, RAG, embeddings, and vector search.
Experience with Azure Data services.
Knowledge of Kafka or other streaming technologies.
Experience with Spark/PySpark.
Knowledge of data cataloging, lineage, and metadata management.
Experience with data pipeline monitoring and observability.
Experience working in a regulated industry such as banking or financial services.

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📌 Data Engineer (Ontario)
🏢 Ampstek
📍 Ontario