Senior Data Architect Cloud Data Platform and AI Enablement

Japheth LLP; Maharashtra; On-site; INR 4,050,000 - 4,950,000; Full time

Job summary

Japheth LLP is looking for a Senior Data Architect to design a cloud-native data platform crucial for enterprise analytics and AI/ML model development. This role involves crafting data architecture aligned with business strategies, establishing real-time data pipelines, and ensuring regulatory compliance.

The ideal candidate should have over 5 years of experience in data architecture, with a strong background in cloud environments, and a relevant degree. The salary offered can go up to 45 lacs, and the position mandates a work-from-office set-up.

Qualifications

• 5+ years in data architecture, with 3+ years in cloud-native environments.
• Proven design of enterprise-scale lakehouses and real-time streaming.
• Experience in regulated fintech, banking, payments, or insurance environments.

Responsibilities

• Design and lead a secure, cloud-native data platform.
• Establish event-driven pipelines using distributed streaming technologies.
• Architect data pipelines optimized for model training and MLOps.

Skills

Data Architecture

Cloud-native environments

Real-time streaming

Distributed processing

Education

Degree in Computer Engineering

Tools

Spark

Kafka

AWS

Oracle

Job description

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: 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: 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‑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.
• Governance Frameworks: Establish governance frameworks aligned with DAMA‑DMBOK principles.
• Data Classification & Retention: 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.
• Governance & Design Reviews: Provide governance through design reviews, technical standards councils, and vendor selection.
• Mentor senior data engineers and architects.

Qualifications

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 up to 45 lacs.