Brillio LLC

Brillio LLC

Data Enterprise Architect - Banking

San Ramon, California, United States

Sponsorship not specifiedDetected 93 days ago
PythonSQLBigQuerySnowflakeRedshiftDatabricksAWSAzureTerraformCI/CDDevOpsPlatform EngineeringKafkaMachine LearningSparkAirflowData EngineeringLLMsRAGLLMOpsMLOpsBusiness DevelopmentTest AutomationLeadership

About the role

  • We are seeking an Enterprise Data Architect to lead client-facing consulting engagements that define enterprise-wide data architecture and guide the delivery of modern data platforms that enable analytics, AI/ML, and digital products at scale. The ideal candidate blends data architecture and applied AI, with demonstrated ability to embed AI into the data
  • engineering lifecycle (requirements discovery, design, development, testing, operations, and governance) to improve delivery speed, quality, reliability, and cost across multiple client contexts. You will work directly with client leadership to shape strategy, create roadmaps, architect solutions, and govern execution from proposal through implementation.

Responsibilities

  • Partner with business and technology stakeholders to translate strategic objectives into scalable data products, domains, and platform capabilities.
  • Lead client discovery and architecture workshops to understand business goals, current-state landscapes, constraints, and value cases; translate these into target-state architecture, migration strategies, and phased roadmaps.
  • Serve as a trusted advisor to client executives and senior stakeholders; drive architecture governance (design authorities), decision logs, and risk management across complex programs.
  • Drive data governance, data quality, privacy, and security-by-design in alignment with enterprise policies and regulatory requirements.
  • Lead and coordinate delivery across client and partner teams (data engineering, BI, ML, security, platform) ensuring scope clarity, dependency management, and adherence to agreed architecture and quality standards.
  • Create high-quality consulting deliverables (architecture decks, reference architectures, ADRs, migration runbooks, governance operating models, and executive readouts) and develop reusable accelerators/templates for repeatable delivery.
  • Provide technical leadership across delivery teams; perform design reviews, resolve architectural risks, and ensure non-functional requirements (performance, cost, resiliency).
  • Support solutioning and business development (RFx): discovery workshops, estimations, capacity planning, proposals, and executive-ready storytelling.

Requirements

  • 10-12+ years of experience in data engineering, data architecture, or platform engineering, including significant experience in client-facing consulting, solution architecture, or program delivery leadership.
  • Working knowledge of AI-enabled engineering practices and/or MLOps fundamentals (model lifecycle, evaluation, monitoring), and how they intersect with data platform architecture and governance.
  • familiarity with feature/embedding stores and RAG pipelines.

Nice to have

  • Experience with domain-driven data architecture and/or data mesh operating models.
  • Experience enabling AI/ML platforms and GenAI/LLM patterns including evaluation/monitoring and Responsible AI governance considerations.
  • Experience institutionalizing AI-assisted delivery for data engineering teams (standards, reusable prompts/templates, secure usage patterns, and productivity/quality measurement).
  • Technical Skills (Representative):
  • MLOps/LLMOps & AI Engineering: model/data versioning, experiment tracking, evaluation, prompt/response logging, safety controls, and integration with deployment/monitoring toolchains

Skills

  • facilitate workshops, synthesize ambiguity into options/trade-offs, and present recommendations to technical and non-technical audiences.
  • Cloud & Data Platforms: Azure/AWS (one or more), Databricks /Snowflake/ BigQuery/ Redshift/ Synapse or equivalent.
  • Data Engineering: Spark, SQL, Python, orchestration (Airflow/ADF/Prefect), streaming (Kafka/ Kinesis/ Event Hubs), ELT/ETL patterns.
  • Architecture & Modeling: dimensional/data vault/3NF patterns, semantic modeling, APIs/data services, enterprise integration patterns.
  • Governance & Security: data catalog/metadata tools, lineage, RBAC/ABAC, encryption, key management, privacy controls.
  • DevOps: CI/CD, IaC (Terraform/Bicep/CloudFormation), automated testing for data pipelines, observability/monitoring.
  • Azure/AWS (one or more), Databricks /Snowflake/ BigQuery/ Redshift/ Synapse or equivalent.
  • data catalog/metadata tools, lineage, RBAC/ABAC, encryption, key management, privacy controls.
  • CI/CD, IaC (Terraform/Bicep/CloudFormation), automated testing for data pipelines, observability/monitoring.
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