Data Architect

• 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).

• 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.

• 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.

• 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

• 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. 

Our commitment to you! 

BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU! 

If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas! 

BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender,  gender expression,  gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran.  If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page.