Data Architect

Responsibilities

• Define and own the data architecture strategy across all environments, platforms, and data domains.
• Establish and maintain architectural standards, patterns, and best practices for data ingestion, storage, modeling, and consumption.
• Design scalable, secure, and resilient data architectures on AWS, including data lake, lakehouse, warehouse, and serving layers.
• Define and champion a Medallion (bronze/silver/gold) architecture, establishing standards for raw, refined, and curated data layers and the promotion patterns between them.
• Lead the platform direction of AgencyBloc's cloud data warehouse/lakehouse, defining how each is used, where workloads run, and how cost and performance are managed.
• Define standards for batch and streaming pipelines, ETL/ELT frameworks, orchestration, and reuse patterns across teams.
• Lead tool and platform selection decisions (warehouse/lakehouse, ingestion, transformation, orchestration, catalog, BI), ensuring alignment with long-term strategy.
• Conduct architecture reviews and provide guidance on complex or high-impact data initiatives.
• Collaborate with engineering leadership to align data capabilities with product and business priorities.

Data Governance, Quality & Compliance

• Define and own the data governance strategy, including data cataloging, lineage, classification, and ownership models.
• Establish data quality standards and frameworks, including validation, monitoring, and remediation practices.
• Partner with security and compliance teams to enforce secure-by-design principles for sensitive and regulated data, including access controls, encryption, masking, and PII handling (SOC 2 and relevant standards).
• Define data retention, archival, and lifecycle management standards.

Observability & Operational Excellence

• Partner with the DevOps Architect to define data pipeline observability standards, including monitoring, alerting, and SLAs/SLOs for freshness, completeness, and reliability, ensuring alerts surface high-quality, actionable signals tied to business impact with minimal noise.
• Establish practices for cost monitoring and optimization across data platforms.

Platform Standardization & Leadership

• Drive standardization and reduction of tool and pattern fragmentation across data teams.
• Mentor data engineers and senior data engineers, providing technical leadership and architectural guidance.

AI & Future Data Strategy

• Define the organization's approach to enabling AI and ML workloads on the data platform, including feature data, governance, and adoption strategy.
• Evaluate emerging data and AI capabilities and incorporate them into the platform roadmap where they provide measurable value.