Data Architect

Techblocks; Hyderabad; Hybrid; INR 4,000,000 - 6,000,000; Full time

Job summary

Techblocks seeks an experienced data architecture leader to design and implement scalable, end-to-end data architectures across ingestion, integration pipelines, and analytics platforms. You will drive data modelling across conceptual, logical, and physical layers to support large-scale financial datasets and reporting needs.

You will also build semantic models, knowledge graphs, and SHACL-based data quality controls while partnering with engineers to ensure reliable delivery and security in

Qualifications

• 15+ years building and operating production data systems.
• Extensive experience in scalable data architectures for large-scale enterprise datasets.
• Strong background in semantic modeling and graph-based solutions.

Responsibilities

• Design and implement end-to-end data architectures across ingestion, pipelines, and analytics.
• Lead data modelling across conceptual, logical, and physical layers.
• Develop and maintain semantic/canonical models and knowledge graphs.
• Collaborate with engineering to ensure architectural integrity and delivery quality.

Skills

Data architecture

Data modelling

Knowledge graphs

Semantic data engineering

Cloud platforms

Production code review

Financial domain knowledge

Agentic MCP integrations

Graph technologies

Leadership

Education

Bachelor's degree in CS or related

Tools

Erwin

RStudio

SPARQL

SHACL

RDF/OWL

Job description

• Design and implement scalable, end-to-end data architectures encompassing source system ingestion, integration pipelines, cloud-native data platforms, data mesh and downstream analytical and operational consumption layers, ensuring robustness, scalability, and security.
• Lead comprehensive data modelling efforts across conceptual, logical, and physical layers, applying best practices in normalized (3NF) and dimensional modelling to support complex, high-volume financial datasets and enable effective reporting and analytics.
• Create and maintain semantic/canonical models using ontologies and validate data integrity via SHACL shapes to build enterprise knowledge graphs, establishing consistent terminology, enforcing data quality constraints, enhancing data interoperability.
• Serve as a trusted, domain-aware technical partner to engineering teams to providing guidance and hands-on support to ensure architectural integrity and delivery excellence.
• Maintain hands-on involvement in reviewing, writing, and improving production code; collaborate with senior engineers to solve complex technical challenges and ensure high-quality deliverables.
• Integrate AI Agents and MCP-based capabilities with enterprise data platforms, enabling secure, governed access to data, tools, and services through scalable and production-ready patterns.
• Design agent-ready data and context architectures leveraging knowledge graphs, semantic models, metadata, and APIs, with strong controls for security, observability, auditability, reliability, and graceful failure.
• Demonstrate practical expertise with cloud platforms (AWS, Azure, GCP), leveraging managed services, multi-tenant architectures, and balancing cost-performance trade-offs effectively.

What are we looking for

• 15+ years building and operating production systems, with increasing depth of expertise at each level.
• Proven hands-on experience in designing and implementing scalable data architecture and comprehensive data modelling (conceptual, logical, and physical) for large-scale enterprise systems using tools like Erwin/RStudio.
• Strong engineering background with the ability and willingness to remain close to the code, implementation, and production environment.
• Strong hands-on expertise in semantic data engineering, ontology development, RDF/OWL, SPARQL, SHACL, and knowledge graph technologies, with experience designing scalable semantic models, validation frameworks, and graph-based solutions.
• Possess strong knowledge of end-to-end workflows relevant to financial domains (e.g., trade lifecycle, processing, reporting, reconciliation), and design data architectures that optimize data movement, quality, lineage throughout and operational resilience throughout these workflows.
• In-depth knowledge about Agentic solutions and MCP-based integrations , with a strong understanding of agentic architectures, enterprise data access, tool integration, security, governance, observability, and production reliability.
• Proven experience building large-scale, distributed, data-intensive, and event-driven systems with cloud-native architectures preferably within financial services or another highly regulated industry.

Leadership & Influence

• Demonstrated ability to lead through technical credibility & hands-on contribution.
• Strong communication skills, with the ability to engage effectively with engineers, engineering managers, product leaders, architects, and senior segment leadership.
• Able to operate effectively across multiple delivery pods, balancing strategic architectural direction with day-to-day engineering realities.
• A strong sense of ownership for outcomes, with the ability to stay engaged from architecture through implementation and production operation.