Senior Data Engineer

Role overview A senior data engineering role responsible for designing and owning the data platforms that feed agentic AI systems serving enterprise and public-sector clients across Canada. The work spans ingestion, warehousing, retrieval indices, and feature stores, with a strong emphasis on secure, compliant pipelines that AI and product teams can rely on.

Responsibilities - Architect batch and streaming data pipelines that unify data from enterprise systems such as EAM, CMMS, ERP, and CRM into analytics- and AI-ready datasets. - Design data warehouses, lakehouses, and retrieval indices — including vector stores — that support LLM, agent, and analytics workloads. - Define data contracts, quality checks, lineage, and observability so downstream AI and product teams can trust what they consume. - Partner with AI engineers on feature engineering, embedding pipelines, and evaluation datasets, and with software engineers on operational integrations. - Lead technical discovery during client engagements by assessing data landscapes, proposing target architectures, and turning them into delivery plans. - Establish standards for security, privacy, and compliance when handling sensitive government, university, and enterprise data.

Requirements - 5+ years of professional data engineering experience, including at least 2 years leading designs for non-trivial data platforms in production. - Deep SQL skills and strong Python, plus comfort with at least one modern transformation framework such as dbt or Spark. - Hands-on experience with orchestration tools like Airflow, Dagster, Prefect, or Azure Data Factory, and cloud warehouses or lakehouses such as Snowflake, BigQuery, Databricks, or Azure Synapse. - Solid understanding of data modelling (dimensional, Data Vault, or similar), schema evolution, and performance tuning. - Experience designing secure pipelines that respect row-level access, PII handling, and auditing requirements. - Clear communicator who can work directly with client stakeholders and mentor other engineers. - Must be legally authorized to work in Canada.

Nice to have - Experience with vector databases such as pgvector, Pinecone, Weaviate, or Azure AI Search and embedding pipelines for RAG systems. - Streaming experience with Kafka, Event Hubs, or Kinesis. - Prior delivery into regulated environments such as government, healthcare, or post-secondary, with familiarity in FOIP, PIPA, or PIPEDA. - Infrastructure-as-code skills (Terraform, Bicep) and mature CI/CD practices for data pipelines.

Benefits and work setup - Full-time senior role based in Edmonton or remote within Canada, with hybrid or remote work modes available.