Principal Data Architect

Responsibilities:

• Design, build, and maintain scalable ELT/ETL pipelines into BigQuery for core domains (orders, customers, products, inventory, fulfillment, procurement, marketing)
• Build orchestration patterns with strong operational rigor (retries, idempotency, backfills, SLAs/SLOs, incident response) to enable reliable delivery
• Implement data quality and testing frameworks (freshness checks, anomaly detection, unit/integration tests) and standardize monitoring/observability
• Standardize CI/CD and infrastructure-as-code patterns for data systems and guide teams on best practices for ingestion, transformation, and orchestration
• Lead modernization initiatives such as legacy warehouse to cloud lakehouse migrations and platform upgrades
• Create and own curated, analytics-ready datasets and models (dimensional/conformed, semantic-ready layers) that make reporting fast, consistent, and self-serve
• Establish and enforce standards for conceptual, logical, and physical data modeling
• Oversee domain-driven modeling and data product design through reusable patterns and reference implementations
• Establish data architecture standards including modeling conventions, schema evolution/versioning, incremental loading strategies, and warehouse performance patterns
• Define and evolve the enterprise data architecture vision aligned to business strategy, including canonical data models, domain boundaries, and integration patterns
• Lead architecture decisions for data warehousing, lakehouse, streaming, MDM, and operational data platforms
• Evaluate and select strategic data technologies and vendors
• Partner with Security and Legal to design privacy and compliance architectures (GDPR/CCPA/SOC2-aligned approaches) and establish enterprise governance frameworks
• Build governance fundamentals including documentation, lineage/metadata, access controls, and PII handling
• Act as the escalation point for data architecture decisions and mentor senior engineers/architects through reviews, templates, and best practices
• Partner with application engineering and analytics stakeholders to define data contracts and ensure reliable upstream/downstream integrations
• Translate business capabilities into scalable data platform solutions
• Optimize cost and performance across BigQuery workloads (query optimization, partition pruning, clustering strategies, and workload management where applicable)
• Drive adoption of emerging capabilities (real-time analytics, AI/ML enablement, semantic layers, data mesh) and develop multi-year data roadmap and maturity models
• Operate at enterprise scope across multiple business domains
• Influence strategy beyond the immediate team and set technical direction that others follow

Requirements:

• Bachelor's degree in CS, Engineering, Information Systems, or a related field
• A minimum of 10 years of experience in Data Engineering, Architecture, or Platform Engineering
• A minimum of 5 years of experience designing enterprise-scale cloud data architectures (AWS, Azure, or GCP)
• Expertise in data warehousing, Lakehouse architecture, distributed processing, streaming, and enterprise data modeling (Kimball, Inmon, Data Vault, and/or domain-driven design)
• Strong understanding of data governance, metadata management, and security architecture
• Experience leading cross-functional architecture initiatives
• Proven ability to influence senior stakeholders and executives

Preferred Qualifications:

• A minimum of 7 years of experience in Data Engineering, Analytics Engineering, or Data Platform roles with significant production ownership
• Deep experience with GCP services relevant to data engineering (e.g., Cloud Storage, Pub/Sub, Dataflow, Cloud Composer, Cloud Functions/Run, Secret Manager, IAM)
• Experience with BigQuery best practices (partitioning/clustering, incremental strategies, performance tuning, cost controls)
• Strong understanding of BI consumption needs (star schema design, incremental refresh considerations, semantic consistency/metrics definitions)
• Experience implementing data mesh or domain-oriented ownership models, AI/ML feature platform exposure, regulated environment experience, and strong cloud cost management acumen
• Industry certifications (AWS Solutions Architect, Azure Architect, GCP Professional Architect)