Every answer our AI gives is only as good as the data underneath it — and we need someone to own that foundation.

AIAEC is an AI-native engineering technology company building agentic AI for AEC (architecture, engineering, construction). Our products run on large volumes of complex, constantly changing external data — and every record has to be trustworthy, governed and ready for AI.

We're seeking a Lead / Principal Data Engineer to take full ownership of our data platform: data acquisition, ingestion, ETL/ELT, data lake and data warehouse, governance, quality and serving, across Azure and/or AWS. The platform is in production — your job is to own it, harden it, scale it, and raise it to the standard an AI-first company needs.

What makes this role different: the primary consumer of our data is AI — agents, retrieval systems and ML models. Data that is "positive enough for a dashboard" is not good enough here. Structure, metadata, versioning and lineage have to be right, every time.

What You'll Own

- The data platform. Full technical ownership of our cloud data platform on Azure and/or AWS — architecture, reliability, security, performance and cost. You decide how it evolves and you are accountable for how it runs.

- All data acquisition. Own and operate our high-volume acquisition from diverse external sources (APIs, web and file-based): scheduling, retries, change detection, source health monitoring and fast recovery when sources change.

- ETL/ELT and processing. Own the distributed pipelines that parse, normalize, deduplicate, enrich and structure raw inputs — including PDFs, scanned documents and images — into canonical, well-tested datasets. Improve throughput, reliability and cost.

- Data lake and data warehouse. Own the layered storage architecture — raw, curated and serving layers — using open table formats, columnar storage and clear conventions for grain, naming, partitioning and audit.

- Data modeling and schema evolution. Own schemas for hierarchical, document-centric and time-versioned data with full history and point-in-time correctness — and the rules for formats, compatibility and versioning, so schemas and data evolve without breaking downstream consumers, including the AI agents and models that depend on them.

- AI-ready data. Ensure every dataset carries rich metadata for precise retrieval and filtering, preserves document structure and relationships, flows through embedding and indexing pipelines into vector and hybrid search stores, and updates incrementally so AI indexes stay current.

- Data versioning for ML. Reproducible, versioned datasets so evaluation sets,



training data and labeled data can be rebuilt exactly at any point in time.

- Data governance. Own ownership and stewardship models, policies and standards, the data catalog, and end-to-end lineage and provenance, so every record traces back to its origin, transformations and version.

- Data quality. Data contracts, automated validation, freshness and completeness checks, reconciliation tests and alerting before bad data reaches products or models. You take point when data is stale, wrong or late.

- Security and compliance. Governed access with role-based access control, cloud identity and access management, service principals and managed identities, secrets management, encryption, auditability, retention, and compliance with Canadian federal and provincial privacy regulations.

- Operational standards. Set and enforce standards for idempotency, late and out-of-order data, replay and backfill, so any pipeline can be safely re-run and any dataset rebuilt.

- Infrastructure and operations. Infrastructure as Code (Terraform), CI/CD for data, automated deployments, monitoring and observability, incident response and FinOps discipline on compute and storage.

- The hard calls. Decide what to fix, what to rebuild and what to leave alone — and where added complexity is worth it versus where a simpler approach is enough.

- The engineering bar. Set standards for development, testing, review, documentation and on-call; lead design reviews and write architecture decision records; mentor engineers; and grow the data team.

All About You

- Experience: 8+ years in data engineering, including 3+ years as a Lead or Senior Data Engineer owning a production data platform end to end — across several companies, so you've seen these patterns work and fail in different organizations.

- Platform Ownership: You've taken over an existing production platform, found its weak points, and made it measurably more reliable, scalable and cost-efficient.

- Data Acquisition at Scale: Proven track record operating high-volume ingestion from diverse, unreliable external sources, including unstructured data.

- Distributed Processing: Advanced experience with distributed data processing engines and performance tuning of large-scale workloads.





- Cloud: Strong hands-on experience with Azure and/or AWS data, compute, storage, serverless, container and identity services; multi-cloud integration experience is a strong plus.

- Data Lake & Data Warehouse: Deep knowledge of data lake, data warehouse and lakehouse architectures, open table formats and columnar file formats.

- Orchestration & Transformation: Production experience with workflow orchestration and layered, well-tested transformation frameworks.

- Data Modeling: Expert SQL and data modeling, including dimensional models, slowly changing dimensions and bitemporal or versioned data.

- Governance & Quality: Data catalogs, lineage, metadata management, data contracts and automated validation — implemented across real teams and production systems, not just read about.

- Document Processing: Experience extracting structured data from PDFs and scanned documents at scale (OCR, layout analysis).

- Engineering Excellence: Expert Python; Git, CI/CD, containers, Terraform and DevOps practices for data platforms.

- Battle Scars: You can walk us through a data platform initiative that went wrong, why it happened, and what you'd do differently now.

- Leadership & Communication: You lead technical design, drive complex initiatives to production, mentor engineers, and explain decisions, trade-offs and risks clearly to engineers and executives — in writing and in person.

- Education: Bachelor's degree in Computer Science, Engineering or a related field, or equivalent practical experience.

Nice to Have

- AI Data Pipelines: Embeddings, vector databases and RAG ingestion in production.

- Dataset Versioning: Versioning and time travel for ML and evaluation datasets.

- Streaming & CDC: Event streaming platforms and change data capture patterns.

- Geospatial: Geospatial data processing and spatial databases.

- Regulated Data: Experience with legal, regulatory or government data.

- Certifications: Azure or AWS data engineering certifications.

Why AIAEC

- Own the foundation. Your data platform is what every AI answer stands on.

- Real ownership and authority — you're accountable for the platform and you decide how it evolves.

- Work at the frontier of applied AI in an industry ripe for disruption — AEC technology is wide-open territory.

- Flexible ways of working, a small, senior team and a real say in technical direction.

If you hold yourself to a high technical bar and want to own the data foundation an AI company runs on — let's talk.

Benefits:

- Flexible schedule
- Paid time off

Work Location: Remote

📌 LEAD / PRINCIPAL DATA ENGINEER (Montreal)
🏢 AIAEC
📍 Montreal