Title: Data Architect (Hands-On Builder)
Location: Canada Remote
Data Architect will focus on the design, build, and evolve a modern, scalable data architecture that will power analytics, reporting, AI/ML use cases, and operational decision-making across the GTM business. This role will define data patterns and standards, partner with IT, and business partner teams to ensure our data platform is secure, reliable, cost-efficient, and easy to use.
This role is ideal for someone with strong hands-on architecture depth and can define target-state architecture, guide implementation, and ensure high-quality delivery across pipelines, integrations, and governance.
Key Responsibilities:
- Build and Own Core Data Pipelines (Hands-On)
- Architect and implement end-to-end ingestion and transformation pipelines from source systems into Snowflake.
- Develop robust ELT/ETL workflows using dbt and orchestration tools (e.g., Airflow/Dagster/ADF), including:
- incremental loads, SCD handling, backfills, and reprocessing
- late-arriving data patterns and idempotent job design
- CDC-based ingestion where applicable
- Build integrations for key enterprise SaaS systems (Salesforce, Marketing Cloud, Netsuite, Zuora, Gainsight, CSOD) and internal app databases.
- Data Modeling & dbt Development
- Own the data modeling layer in Snowflake with dbt:
- design dimensional models (star schemas), marts, and curated layers
implement dbt best practices: staging intermediate- marts, modular models, macros, packages
- define metric-ready datasets (e.g., ARR/NRR, pipeline, churn, product usage) with consistent definitions
- Optimize for performance and cost (clustering, warehouse sizing, query patterns, caching, micro-partitioning awareness).
Cloud Platform Enablement (AWS + Azure)
- Implement secure, scalable data platform components across AWS and Azure:
- landing zones, storage (S3/ADLS), networking, secrets management, and compute integration patterns
- secure connectivity to Snowflake (PrivateLink/peering patterns where relevant)
- Work with technology teams to implement RBAC, role hierarchy, masking policies, row access policies, and data sharing patterns.
Reliability, Testing, and Observability
- Implement data quality and reliability controls:
- dbt tests (schema, relationship, accepted values) and custom tests for business logic
- anomaly detection and pipeline monitoring (e.g., Datadog/CloudWatch/Azure Monitor, Monte Carlo if applicable)
- SLAs/SLOs for critical datasets and clear incident runbooks
- Build operational readiness: logging, alerting, retries, failure isolation, and safe deploys.
CI/CD, Version Control, and Engineering Practices
- Build and maintain CI/CD for data development (PR checks, dbt builds, environment promotion).
- Establish practical engineering standards:
- naming conventions, repo structure, branching strategy
- documentation that stays current (dbt docs, data dictionaries)
- code reviews and design patterns that scale with the team
AI / Agentic AI Architecture
- Design and implement architecture patterns that support AI/ML, GenAI, and agentic AI use cases across the Sales business, including structured and unstructured data pipelines, retrieval-ready data design, and secure enterprise data access.
- Define scalable patterns for AI-enabled applications and agents, including metadata design, indexing, vector-ready data preparation, API/tool access, and governance controls such as lineage, auditability, observability, and guardrails.
- Partner Closely with Analytics & Business Teams
- Collaborate with BI/Analytics to ensure the curated layer supports dashboards and self-service.
- Translate business needs into data products quickly with tight feedback loops, iterative delivery, measurable outcomes.
