Data Architect

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Overview

LABUR is supporting a client as it expands its Data and AI capabilities to deliver real-time, predictive insights across the full lifecycle of its operations. This engagement focuses on designing, building, and operating a scalable semantic layer and data foundation that makes data discoverable, trusted, secure, and ready for advanced analytics and agentic AI use cases.The work includes creating reusable data models and metrics, embedding governance as code, and implementing automated data quality, lineage, and access controls to ensure data products meet defined standards for reliability, availability, and semantic interoperability.

Responsibilities

• Own the target architecture for Databricks on AWS, including S3/EC2, Delta Lake, Unity Catalog, MLflow/Model Registry, Delta Live Tables, and integrations with BI tools, IDEs, agentic AI platforms, and downstream applications.
• Design scalable ingestion and serving patterns, data contracts, and API/SQL access layers for internal consumers and AI-driven use cases.
• Plan and guide platform evolution, including multi-workspace governance, environment separation, lineage, and observability, while leading upgrades and migrations with minimal disruption.
• Champion infrastructure-as-code and CI/CD practices (e.g., Terraform, GitHub Actions) to enable reproducible workspaces, catalogs, pipelines, and policies.
• Deploy and maintain an enterprise data catalog within Databricks.
• Design and evolve the semantic layer, including subject-area models, conformed dimensions, and governed metrics exposed for BI, APIs, and AI agents.
• Refactor business logic currently embedded in dashboards or ad hoc SQL into reusable transformations, tests, and standardized metric definitions.
• Implement and maintain end-to-end lineage, technical and business metadata, and documentation within Unity Catalog, ensuring metadata is optimized for AI and RAG use cases.
• Lead the scaling of master data domains (e.g., projects, vendors, budgets, people) and define golden record rules.
• Embed rule-based and ML-driven anomaly detection into pipelines and the semantic layer.
• Define, monitor, and report on SLAs, supported by clear incident response playbooks.
• Drive continuous improvement initiatives to significantly reduce data quality issues year over year.
• Partner with information security teams to maintain secure cloud infrastructure for analytics and AI workloads.
• Develop and enforce data classification, access controls, and privacy frameworks across the analytical stack.
• Own annual assessments and audits related to analytics and data platforms.
• Ensure compliance with applicable data regulations, internal policies, and governance standards.
• Establish and maintain model registries, audit trails, and bias/drift monitoring for predictive and generative AI models.
• Partner with Data Science teams to integrate governance checkpoints throughout model development and deployment pipelines.
• Champion a “data-as-a-service” culture by making trusted, well-governed data the default for teams across the organization.
  
  

Qualifications

• 5 years of experience in data architecture or data engineering within a modern cloud environment, including 2 years owning a semantic or metrics layer at scale.
• Strong SQL and Python skills, with hands-on experience using Delta Lake, Unity Catalog, MLflow/Model Registry, and Delta Live Tables (or equivalent transformation frameworks).
• Demonstrated success implementing enterprise data cataloging, lineage, and automated data quality frameworks.
• Working knowledge of access control patterns, data classification, and PII protection in analytical platforms; proven ability to partner effectively with security teams.
• Excellent communication and stakeholder management skills, with the ability to translate governance and architecture concepts to both technical and non-technical audiences.
  
  

Compensation

$150,000 - $155,000 per year - Dependent on fit and experience.