Principal Architect II - Technology

PeopleStrong; India; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

PeopleStrong in Hyderabad, India seeks a Data Architect to define, design, and govern enterprise data architecture across operational, analytical, and AI-enabled platforms. You will own the data architecture strategy, data models, schemas, and integration patterns to deliver trusted, scalable data products.

Collaborate with product, engineering, analytics, security, and compliance stakeholders to ensure data is reliable, discoverable, and compliant with policy standards.

Qualifications

• 12+ years of experience in data architecture and enterprise data platforms.
• Proven expertise in ETL/ELT, data modeling, and data governance.
• Hands-on with cloud data platforms (AWS/Azure) and AI-ready data assets.

Responsibilities

• Define and own enterprise data architecture, standards, and platform roadmaps.
• Design data lake/lakehouse/warehouse architectures for structured and unstructured data.
• Lead data modeling across transactional and analytical domains with scalable patterns.
• Ensure data cataloging, lineage, governance, and security controls across pipelines.

Skills

Enterprise data architecture

ETL/ELT design

Advanced SQL

Data modeling

Schema design

Data governance

Cloud data platforms

Python

Data security

Data cataloging

Education

MS/ME/BE/MCA/M.Tech in CS/Information Systems/Data Engineering

Tools

AWS

Azure

SageMaker

Job description

• TELANGANA

Posted On 27 Aug 2026

End Date 10 Sep 2026

Required Experience 14 - 18 years

Basic Section

No. Of Openings 1

Grade T4C

Closing Date 10 Sep 2026

Organisational

Country IN

State TELANGANA

City HYDERABAD

Job Title

Data Architect

Role Summary

The Data Architect is a senior technical leadership role responsible for defining, designing, and governing enterprise data architecture across operational, analytical, and AI-enabled data platforms. This role owns the data architecture strategy, data models, schemas, integration patterns, performance standards, data cataloging, lineage, golden data-set design, and data security controls required to deliver trusted, scalable, and AI-ready data products. The Data Architect works closely with product, engineering, analytics, AI/ML, security, compliance, and business stakeholders to ensure structured and unstructured data is reliable, governed, discoverable, reusable, and aligned to enterprise data policy.

Educational Qualification

• MS/ ME / BE / MCA / M.Tech / MS in Computer Science, Information Systems, Data Engineering, or related discipline
• Advanced certification or specialization in Data Architecture, Cloud Data Platforms, Data Governance, or Analytics preferred

Experience

• 12+ years of experience in data architecture, data engineering, enterprise data platforms, data modeling, data warehousing, lakehouse/data lake architecture, and large-scale data integration
• Proven experience designing production-grade enterprise data platforms with strong focus on performance, scalability, reliability, security, governance, and maintainability
• Hands-on experience with ETL/ELT pipelines, SQL optimization, schema design, metadata management, data cataloging, data lineage, and golden data-set/reference data architecture
• Experience enabling AI/ML use cases through AI-ready datasets, feature-ready data assets, data quality standards, and cloud data integration patterns including AWS SageMaker data preparation workflows
• Experience with languages like python, bash/ shell scripting, Go, et. al. and data-flow management tools

Key Responsibilities

Data Architecture & Strategy

• Define and own the enterprise data architecture, data standards, data design principles, and platform roadmap across operational, analytical, reporting, and AI/ML use cases.
• Design scalable data lake, lakehouse, warehouse, and domain-aligned data product architectures that support high-volume structured and unstructured data.
• Establish architectural patterns for ingestion, transformation, storage, consumption, data sharing, and interoperability across enterprise systems.
• Drive architecture reviews and data design governance to ensure solutions meet enterprise data policy, security, compliance, and audit expectations.
• Architect robust ETL/ELT pipelines for batch, near-real-time, and event-driven data processing
• Define reusable integration patterns for source-system onboarding, schema evolution, data validation, reconciliation, error handling, and operational observability.
• Guide engineering teams on pipeline design for performance, fault tolerance, recovery, monitoring, and cost optimization.
• Ensure pipelines maintain metadata, lineage, quality metrics, and traceability from source to curated/golden data layers.

Data Modeling, Schema Design & Performance

• Lead conceptual, logical, and physical data modeling across transactional, analytical, dimensional, and domain-oriented data structures.
• Own schema design standards, naming conventions, data contracts, partitioning strategies, indexing approaches, and query optimization guidelines.
• Optimize data models and SQL patterns for performance, concurrency, latency, scalability, and cost across databases, warehouses, and distributed data platforms.
• Define standards for schema versioning, schema change impact analysis, backward compatibility, and controlled migration from legacy to modern data structures.

AI Data Readiness & Golden Data-Sets

• Define AI data readiness standards for completeness, consistency, provenance, explainability, quality, privacy, and usability in analytics, ML, GenAI, and agentic AI workflows.
• Architect golden data-sets, canonical data models, reference data, and master data patterns to support trusted downstream reporting, automation, and AI decision support.
• Partner with AI/ML teams to prepare fit-for-purpose data assets for model training, evaluation, RAG pipelines, feature engineering, and AWS SageMaker-based workflows where applicable.
• Ensure structured and unstructured data assets are curated with metadata, access controls, retention rules, quality checks, lineage, and business definitions.
• Establish and enforce enterprise data governance standards covering data ownership, stewardship, classification, sensitivity, retention, usage, quality, and lifecycle management.
• Define data catalog and business glossary practices so data assets are discoverable, well-described, classified, and reusable across product, analytics, and AI teams.
• Ensure end-to-end lineage is captured across ingestion, transformation, curation, consumption, and archival stages to support auditability, compliance, and impact analysis.
• Translate enterprise data policy into technical design controls, including access management, row/column-level security, encryption, masking, tokenization, and audit logging.
• Architect and govern data solutions on AWS, Azure, or hybrid cloud platforms using modern data storage, orchestration, processing, cataloging, and analytics services.
• Provide architectural guidance for AWS SageMaker data preparation and AI/ML integration patterns, ensuring data assets are secure, governed, and production-ready.
• Define standards for CI/CD, infrastructure-as-code alignment, data deployment practices, environment promotion, monitoring, alerting, and operational readiness.
• Ensure reliability, scalability, availability, maintainability, and cost efficiency of data platforms and data pipelines in production.
• Act as Principal Architect and technical mentor for Data Engineers, Analytics Engineers, BI Engineers, and platform teams.
• Lead data architecture reviews, data design forums, data governance forums, and technical decision-making for strategic data initiatives.
• Collaborate with product, engineering, security, compliance, analytics, AI/ML, operations, and business stakeholders to align data solutions to measurable business outcomes.
• Drive innovation while ensuring delivery discipline, documentation quality, and sustainable engineering practices in Agile environments.

Required Skills & Expertise

• Enterprise data architecture across data lakes, lakehouses, warehouses, marts, operational stores, and data products
• Strong ETL/ELT architecture and hands-on understanding of ingestion, transformation, orchestration, reconciliation, and observability patterns
• Advanced SQL, data modeling, schema design, dimensional modeling, normalization/denormalization, data contracts, and schema evolution practices
• Performance engineering for data platforms, including indexing, partitioning, caching, query optimization, concurrency management, and cost optimization
• Structured and unstructured data architecture, including documents, notes, logs, transcripts, JSON/XML, APIs, events, and analytical datasets
• Data catalog, metadata management, business glossary, end-to-end lineage, impact analysis, and data quality frameworks
• Golden data-set, canonical data model, master/reference data, and trusted data layer design
• AI data readiness for analytics, ML, GenAI, RAG, feature engineering, and AWS SageMaker or equivalent AI/ML platform integration
• Data governance, data policy implementation, data security, privacy, access controls, encryption, masking, tokenization, and auditability
• Cloud data platforms on AWS and/or Azure, with awareness of storage, compute, orchestration, catalog, analytics, and AI data preparation services
• Ability to lead architecture reviews, mentor engineering teams, and influence product/platform decisions across cross-functional stakeholders

Preferred / Nice to Have

• Healthcare, RCM, claims, clinical, payer/provider, or other regulated-domain data architecture experience
• Experience with interoperability and healthcare data standards such as FHIR, HL7, X12/EDI, or payer/provider integration patterns
• Exposure to data mesh, domain data products, lakehouse architecture, medallion architecture, and modern semantic layer practices
• Experience supporting AI/ML governance, model evaluation datasets, human-review workflows, explainability, and audit-ready data assets
• Familiarity with data observability, data contracts, data quality automation, privacy-enhancing technologies, and responsible AI data governance
• Experience defining enterprise data strategy, modernization roadmaps, migration approaches, and platform adoption across large engineering teams