Senior Data Architect Cloud Data Platform AI Enablement
Role Overview
We are seeking a Senior Data Architect to design and lead a secure, cloud-native data platform serving as the foundation for enterprise analytics, AI/ML model development, and real-time financial decisioning. This strategic role enables intelligent automation, risk-aware growth, and regulatory-grade data transparency.
WORK FROM OFFICE ROLE AND FACE TO FACE INTERVIEW MANDATORY.
Key Responsibilities
- Data Architecture Platform Design
- Target State: Define and own the enterprise data architecture aligned to business and regulatory strategy.
- Lakehouse Structure: Design and implement a cloud-native lakehouse architecture using multi-tier storage patterns (Bronze/Silver/Gold layers).
- Real-Time Pipelines: Establish event-driven pipelines using distributed streaming technologies.
- Data Mesh: Lead adoption of decentralized data mesh principles for domain-oriented ownership.
- Ingestion Frameworks: Architect scalable ingestion from transactional systems, payment rails, APIs, and third-party financial data providers.
- Standards Operations: Define metadata-driven design standards, schema evolution, high availability, performance optimization, and disaster recovery.
- AI/ML Enablement
- ML Pipelines: Architect data pipelines optimized for model training, feature engineering, and MLOps.
- Feature Store: Design a governed feature store architecture to support reusable ML features.
- Real-Time Decisioning: Enable low-latency pipelines for live fraud detection, credit risk scoring, and personalization.
- ML Workloads: Align storage formats, partitioning strategies, and compute models to ML workloads; establish automated model monitoring, lineage, and reproducibility.
- Integration: Connect model serving environments with secure API gateways and core decision engines.
- Security Compliance Architecture
- Security-by-Design: Embed security across ingestion, storage, transformation, and access layers.
- Cryptography: Architect data encryption strategies (at rest, in transit, and key management lifecycle).
- Access Control: Implement fine-grained controls using IAM, RBAC, and ABAC frameworks under zero-trust principles.
- Auditability: Ensure immutable logging, audit trails, and traceability for regulatory inspections.
- Compliance Alignment: Partner with InfoSec/Risk to meet SOC 2, PCI-DSS, ISO 27001, GDPR, and financial regulations.
- Data Governance Risk Management
- Frameworks: Establish governance frameworks aligned with DAMA-DMBOK principles.
- Policies: Define data classification, retention, archival, and purging rules.
- Data Quality: Implement end-to-end data lineage, automated validation, anomaly detection, and reconciliation controls.
- Marts: Architect regulatory reporting data marts with traceable transformation logic and formalized domain stewardship.
- Leadership Collaboration
- Translate business objectives into architectural blueprints and execution roadmaps for cross-functional stakeholders.
- Provide governance through design reviews, technical standards councils, and vendor selection.
- Mentor senior data engineers and architects.
Qualifications Tech Stack
Required Technical Domain Expertise
- Experience: 5+ years in data architecture, with 3+ years explicitly in cloud-native environments.
- Education: Degree in Computer Engineering or related discipline.
- Technical Mastery: Proven design of enterprise-scale lakehouses, distributed processing (Spark), real-time streaming (Kafka), dimensional modeling, data vault, and semantic layers.
- Domain: Demonstrated infrastructure experience in regulated fintech, banking, payments, or insurance environments supporting production AI/ML workloads.
- Compliance Competence: Hands-on architecture experience in environments subject to SOC 2, PCI-DSS, ISO 27001, IAM frameworks, encryption standards, and GDPR.
- Leadership: Proven track record heading cross-functional initiatives with strict Architecture Decision Record (ADR) discipline.
Preferred Qualifications
- Cloud-native data platform design on AWS or Oracle within financial institutions.
- Exposure to MLOps frameworks, AI orchestration, and feature store designs.
- Professional certifications (Cloud Architect, Data Engineering, or Security).
- Background in credit risk, fraud analytics, or payment ecosystems.
Maximum salary upto 45 lacs.
This job is provided by Shine.com
