Data Enterprise Architect Banking
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Job Location:
Edison, NJ - USA
Monthly Salary:
Not Disclosed
Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
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.
Job Responsibilities:
Define enterprise data architecture principles target-state blueprints and reference architectures (data platforms integration governance and consumption patterns).
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.
Lead architecture for cloud/hybrid data solutions including data lakes/lakehouses warehouses streaming and real-time analytics.
Establish data modeling standards (conceptual/logical/physical) metadata strategy master/reference data patterns and semantic layers.
Drive data governance data quality privacy and security-by-design in alignment with enterprise policies and regulatory requirements.
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.
Lead architecture for cloud/hybrid data solutions including data lakes/lakehouses warehouses streaming and real-time analytics.
Establish data modeling standards (conceptual/logical/physical) metadata strategy master/reference data patterns and semantic layers.
Drive data governance data quality privacy and security-by-design in alignment with enterprise policies and regulatory requirements.
Architect and guide implementation of ingestion transformation orchestration and CI/CD for data pipelines (batch and streaming).
Embed AI into the data engineering lifecycle by defining standards and guardrails for AI-assisted development (code generation/review test generation documentation) and by establishing measurable quality gates (data tests pipeline tests and regression checks).
Architect data foundations for AI initiatives partnering with ML/AI teams on MLOps/LLMOps integration training/inference data management feature/embedding stores (where applicable) and evaluation/monitoring telemetry.
Ensure Responsible AI and compliance considerations are reflected in data architecture: sensitive data handling consent/retention access controls auditability and end-to-end lineage across data pipelines and AI artifacts.
Enable AI/ML and GenAI use cases by designing feature-ready datasets vector/search patterns where applicable and governance for model/data lineage.
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.
Embed AI into the data engineering lifecycle by defining standards and guardrails for AI-assisted development (code generation/review test generation documentation) and by establishing measurable quality gates (data tests pipeline tests and regression checks).
Architect data foundations for AI initiatives partnering with ML/AI teams on MLOps/LLMOps integration training/inference data management feature/embedding stores (where applicable) and evaluation/monitoring telemetry.
Ensure Responsible AI and compliance considerations are reflected in data architecture: sensitive data handling consent/retention access controls auditability and end-to-end lineage across data pipelines and AI artifacts.
Enable AI/ML and GenAI use cases by designing feature-ready datasets vector/search patterns where applicable and governance for model/data lineage.
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.
Job Requirements:
1012 years of experience in data engineering data architecture or platform engineering including significant experience in client-facing consulting solution architecture or program delivery leadership.
Proven experience designing and implementing enterprise-scale data platforms (lake/lakehouse/warehouse) on cloud and/or hybrid environments.
Strong foundation in data modeling integration patterns distributed processing and performance tuning with the ability to apply these to AI-ready data products (training/inference readiness feature readiness and observability).
Hands-on understanding of governance disciplines: data quality catalog/metadata lineage privacy retention and access controls.
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.
Excellent consulting communication skills: facilitate workshops synthesize ambiguity into options/trade-offs and present recommendations to technical and non-technical audiences.
Ability to lead cross-functional stakeholders communicate architecture decisions and influence senior leaders
Proven experience designing and implementing enterprise-scale data platforms (lake/lakehouse/warehouse) on cloud and/or hybrid environments.
Strong foundation in data modeling integration patterns distributed processing and performance tuning with the ability to apply these to AI-ready data products (training/inference readiness feature readiness and observability).
Hands-on understanding of governance disciplines: data quality catalog/metadata lineage privacy retention and access controls.
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.
Excellent consulting communication skills: facilitate workshops synthesize ambiguity into options/trade-offs and present recommendations to technical and non-technical audiences.
Ability to lead cross-functional stakeholders communicate architecture decisions and influence senior leaders
Preferred Qualifications:
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).
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):
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.
MLOps/LLMOps & AI Engineering: model/data versioning experiment tracking evaluation prompt/response logging safety controls and integration with deployment/monitoring toolchains; familiarity with feature/embedding stores and RAG pipelines.
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.
MLOps/LLMOps & AI Engineering: model/data versioning experiment tracking evaluation prompt/response logging safety controls and integration with deployment/monitoring toolchains; familiarity with feature/embedding stores and RAG pipelines.
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
Staff IC
About Company
Brillio is a global leader in Enterprise Digital Transformation Solutions, providing strategic consulting services and solutions using emerging technologies.
