Senior Data Engineer

About the Role

At Triomind, we build Agentic AI frameworks that sit alongside enterprise systems to automate complex workflows across facilities, assets, and capital programs. This is a role for engineers who want to ship AI that real institutions depend on — not another demo.

What You'll Do

• Architect and implement batch and streaming data pipelines that unify data from enterprise systems (EAM, CMMS, ERP, CRM) and operational data stores 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; partner with software engineers on operational integrations.
• Lead technical discovery during client engagements: assess existing data landscapes, propose target architectures, and translate them into actionable delivery plans.
• Establish standards for security, privacy, and compliance when handling sensitive government, university, and enterprise data.

What We're Looking For

• 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; comfortable with at least one modern transformation framework (dbt, Spark, or equivalent).
• Hands-on experience with orchestration tools (Airflow, Dagster, Prefect, or Azure Data Factory) and cloud data warehouses / lakehouses (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 (pgvector, Pinecone, Weaviate, Azure AI Search) and embedding pipelines for RAG systems.
• Streaming experience with Kafka, Event Hubs, or Kinesis.
• Prior delivery into regulated environments (government, healthcare, post-secondary) and familiarity with FOIP, PIPA, or PIPEDA.
• Infrastructure-as-code skills (Terraform, Bicep) and mature CI/CD practices for data pipelines.

Tech We Use