Data Architect

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.

ETL/ELT, Integration & Data Pipelines
- 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 up-to-date 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.

Data Governance, Catalog, Lineage & Policy
- 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.

Cloud Data Platforms & Production Readiness
- 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.

Leadership, Collaboration & Mentorship
- 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

Role Level
- Principal / Architect

📌 Principal Data Architect (Hyderabad)
🏢 Omega Healthcare
📍 Hyderabad