This role is for one of the Weekday's clients

Salary range: Rs 3000000 - Rs 4500000 (ie INR 30 - 45 LPA)

Min Experience: 10+ years

Location: Hyderabad, Telangana, India
JobType: full-time

We are seeking an experienced Senior Architect to define and own the architectural patterns for a modern enterprise data ingestion and integration platform. The role will focus on designing scalable batch and streaming frameworks that enable reliable, governed, and high-performance movement of data from source systems into a cloud-based Lakehouse environment.

You will work across architecture, engineering, data platform, governance, and SRE teams to establish technical standards, drive platform modernization, and ensure end-to-end architectural consistency. This role offers an opportunity to influence large-scale distributed systems, cloud-native data platforms, real-time streaming, and emerging AI-driven data capabilities.

Requirements

Key Responsibilities

Architecture & Technical Leadership

• Define and own reference architectures for enterprise-scale batch and streaming data ingestion frameworks.
• Design scalable, resilient, and high-throughput architectures for moving data from source systems into modern Lakehouse platforms.
• Establish technical standards and architectural patterns for event-driven and near real-time data pipelines.
• Evaluate emerging technologies and recommend appropriate platform investments and modernization strategies.
• Lead architecture reviews, design discussions, technical evaluations, and architecture governance processes.
• Make critical architectural decisions while balancing scalability, reliability, performance, security, cost, and maintainability.
• Mentor engineers and architects and help raise the overall technical and architectural maturity of the organization.

Data Platform Architecture

• Design and evolve modern Lakehouse architectures for enterprise data platforms.
• Define patterns around Delta Lake, data products, metadata, governance, and scalable data integration.
• Architect batch and real-time ingestion pipelines supporting diverse data sources and consumers.
• Ensure data platforms are designed for reliability, scalability, security, and efficient downstream consumption.
• Establish architectural patterns for integrating data across analytics, applications, AI systems, and other enterprise consumers.

Streaming & Distributed Systems

• Design high-throughput, event-driven architectures for near real-time data processing.
• Develop architectural standards around Kafka, Redis, CDC, and streaming frameworks.
• Define patterns for reliable event processing, data movement, fault tolerance, scalability, and recovery.
• Ensure streaming platforms meet enterprise performance, availability, and reliability requirements.

Cloud & Infrastructure Architecture

• Design cloud-native data platform architectures using Microsoft Azure and related services.
• Establish patterns for services such as Azure Data Factory, Event Hubs, Blob Storage, ADLS Gen2, and Azure DevOps.
• Define and standardize Infrastructure as Code (IaC) practices using Terraform or comparable technologies.
• Establish repeatable, governed infrastructure deployment and configuration standards.
• Partner with engineering and DevOps teams to improve CI/CD, automation, and platform reliability.

Reliability & Governance

• Partner with SRE teams to define and implement appropriate SLAs, SLOs, observability, and reliability standards for data infrastructure.
• Ensure architectural designs incorporate monitoring, alerting, resiliency, security, and operational readiness.
• Collaborate with governance and platform teams to ensure data ingestion follows enterprise security, compliance, and governance standards.
• Identify architectural risks and proactively develop mitigation strategies.

AI & Emerging Technologies

• Explore and architect AI capabilities that improve the efficiency, intelligence, and optimization of data ingestion platforms.
• Design data infrastructure patterns that support AI, machine learning, and intelligent automation use cases.
• Evaluate emerging technologies and determine their applicability to enterprise data platforms.
• Enable scalable data architectures capable of supporting evolving AI and analytics requirements.

Requirements

Must-Have Skills

• 7+ years of experience in systems engineering, solution architecture, or technical architecture, with strong exposure to data platforms and distributed systems.
• Proven experience defining reference architectures for enterprise-scale data integration and data platforms.
• Strong expertise in Data Platform Architecture, including Lakehouse architectures, Delta Lake, and cloud-based data platforms.
• Strong experience with Azure Databricks and modern enterprise data architectures.
• Hands-on experience designing Kafka / Streaming Frameworks and event-driven architectures.
• Strong understanding of Infrastructure as Code and CI/CD practices.
• Experience working with cloud-native technologies and distributed systems.
• Strong understanding of data ingestion, integration, processing, and platform engineering concepts.
• Excellent communication and stakeholder management skills with the ability to influence senior technical and business stakeholders.

Good-to-Have Skills

• Strong experience with Kafka and enterprise streaming platforms.
• Hands-on experience with Terraform or comparable Infrastructure as Code tools.
• Experience with Azure Data Factory, Azure Event Hubs, ADLS Gen2, Blob Storage, and Azure DevOps.
• Experience with Redis, Change Data Capture (CDC), and event-driven architectures.
• Experience with Unity Catalog and modern data governance practices.
• Experience designing data infrastructure for AI/ML use cases.
• Prior architecture leadership in large enterprise data integration or data platform engineering environments.
• Experience leading enterprise platform modernization or migration initiatives.

Educational Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Engineering, or a related technical discipline.
• Equivalent experience in enterprise architecture and large-scale data platforms may also be considered.

Ideal Candidate Profile

The ideal candidate is a hands-on and strategic architect who can operate across enterprise architecture, cloud platforms, distributed systems, data engineering, and streaming technologies.

You should be comfortable defining long-term architectural direction while also engaging deeply with engineering teams on implementation details. Strong ownership, technical judgment, communication, and the ability to influence architecture decisions across multiple teams are essential.

Success Metrics

Success in this role will be measured by the scalability, reliability, performance, and governance of the enterprise data ingestion architecture. Key outcomes include successful adoption of reference architectures, reliable batch and streaming pipelines, improved platform observability, infrastructure automation, architectural consistency across teams, and successful enablement of analytics and AI-driven data use cases.