Data Enterprise Architect - Banking

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., * 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.

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.

• 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.
• 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).

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.