Title and Location Data Architect (Hands-On Builder) Location: Mexico Remote Overview 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. 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 to intermediate to 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. Required Qualifications 10+ years of experience in data engineering and data architecture with a strong hands-on track record. Strong production experience with Snowflake (performance tuning, data loading patterns, security model). Strong production experience with dbt: incremental models, snapshots, tests, macros, documentation, deployments Experience building pipelines and integrations in AWS and/or Azure (both preferred). Advanced SQL skills and strong understanding of data modeling (dimensional modeling, SCD types). Experience with orchestration (Airflow, Dagster, Prefect, or Azure Data Factory) and reliable job design. Strong understanding & framework implementation of data quality, monitoring, and operational best practices. Preferred Qualifications (Nice To Have) Experience with ingestion tooling (Boomi, Fivetran, Matillion, Informatica, Stitch) and/or CDC tooling (Debezium/HVR). Experience designing or supporting data architecture for GenAI, RAG, enterprise search, or agentic AI use cases in production environments. Familiarity with modern AI-related technologies and patterns such as OpenAI APIs, LangChain/LangGraph, Snowflake Cortex, Azure AI services, vector-ready data pipelines, and secure tool integration for AI agents. Experience with streaming/event ingestion (Kafka/Kinesis/Event Hubs) for near-real-time pipelines. Familiarity with governance/security patterns in Snowflake (masking/row access, tags, classification). Exposure to BI layers (Tableau/Power BI/Looker) and semantic/metrics frameworks. Python experience for custom integrations, APIs, and automation (Lambda, Azure Functions). Equal Opportunity Employment Statement 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. BMC Software maintains a strict policy of not requesting any form of payment in exchange for employment opportunities, upholding a fair and ethical hiring process. #J-18808-Ljbffr