Data Architect

You may occasionally be required to travel for business

This role can be based remotely in Canada

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BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage.

Title:
Data Architect (Hands-On Builder)

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.

• 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
• 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).
• 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 (Private Link/peering patterns where relevant)
• Work with technology teams to implement RBAC, role hierarchy, masking policies, row access policies, and data sharing patterns.

• 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/Cloud Watch/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.

• Build and maintain CI/CD for data development (PR checks, dbt builds, environment promotion).
• naming conventions, repo structure, branching strategy
• documentation that stays current (dbt docs, data dictionaries)
• code reviews and design patterns that scale with the team

• 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.

• 10+ years of experience in data engineering and…