Cloud Data Architect | Cloud, Ai/ml, Data Modernization
- Innominds Software
- 10 - 20 years
- Hyderabad
- 18 hours ago
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Job Description
Location: Hyderabad
Role: Data Cloud Architect
Years of Experience: 12-25+Yrs
Qualification: BE/B.TECH/ME/M.TECH/MCA/MSc (Computers)
Data Cloud Architect: Architect Leadership Ro*e@In******s
Role Overview
We are seeking a senior Data Cloud Architect to lead enterprise-scale data platform modernization initiatives. This role combines architectural authority, business consulting, platform engineering, and customer advisory leadership.
The architect will design and operationalize cloud-native data ecosystems that enable advanced analytics, AI/ML, and real-time intelligence. They will serve as a trusted advisor to executive stakeholders, guiding data transformation strategies while ensuring scalable, secure, and resilient architecture delivery.
The ideal candidate is a hands-on architectural leader capable of shaping multi-cloud data platforms, influencing senior decision-makers, and driving innovation in modern data engineering practices.
Core Responsibilities
Practice Strategy & Architecture Leadership
- Define and execute the strategic roadmap for data engineering and cloud analytics capabilities.
- Develop reusable architecture blueprints and modernization frameworks.
- Drive innovation in:
- Lakehouse architectures
- Data mesh/data fabric models
- AI-enabled pipelines
- Real-time analytics platforms
- Align architecture strategy with organizational growth goals.
- Mentor senior architects and engineering leaders.
Enterprise Data Architecture & Technical Excellence
- Architect scalable cloud-native data platforms across AWS, Azure, or GCP.
- Lead architecture design for:
- Batch + streaming data pipelines
- Lakehouse/data warehouse modernization
- Metadata-driven ingestion frameworks
- Distributed processing platforms
- AI/ML-ready data ecosystems
- Define enterprise data modeling standards.
- Establish data lifecycle, lineage, and quality frameworks.
- Optimize performance, scalability, and cost efficiency.
Cloud Data Platform Engineering
- Design landing zones for data workloads.
- Architect orchestration frameworks and pipeline automation.
- Enable CI/CD for data engineering.
- Integrate observability and data reliability engineering.
- Define storage and compute optimization strategies.
AI/ML & Advanced Analytics Integration
- Architect pipelines supporting AI/ML workflows.
- Enable feature engineering and model data readiness.
- Integrate generative AI workflows with data platforms.
- Support scalable model training and inference data pipelines.
Governance, Security & Compliance Architecture
- Define enterprise data governance frameworks.
- Architect access control and data privacy models.
- Ensure compliance readiness.
- Implement data cataloging and lineage strategies.
Customer Advisory & Executive Engagement
- Act as strategic advisor to CIO/CDO/Analytics leadership.
- Conduct architecture discovery workshops.
- Translate business goals into platform roadmaps.
- Present architecture strategy to executive audiences.
- Lead architecture reviews and modernization planning.
- Support pre-sales solution positioning.
Delivery Oversight & Transformation Governance
- Provide architecture governance for large-scale programs.
- Define engineering quality gates.
- Identify delivery risks and mitigation strategies.
- Ensure alignment with agile and DevOps practices.
- Lead architecture retrospectives and continuous improvement.
Practice Growth & Thought Leadership
- Develop reusable accelerators and frameworks.
- Enable sales teams with solution narratives.
- Represent the organization in technical forums.
- Drive internal capability uplift.
Technical Expertise Expectations
Cloud Platforms
- AWS / Azure / GCP data ecosystem services
- Multi-cloud architecture patterns
- Hybrid integration
Data Engineering
- ETL/ELT frameworks
- Distributed processing engines
- Streaming architectures
- Lakehouse/data warehouse modernization
Data Architecture
- Dimensional modeling
- Data mesh/data fabric patterns
- Metadata-driven design
Platform Engineering
- Pipeline automation
- CI/CD for data systems
- Infrastructure-as-code
AI/ML Integration
- Data preparation for ML
- Feature pipelines
- GenAI data pipelines
Governance & Security
- Data privacy frameworks
- Access control models
- Lineage & cataloging
Reliability & Observability
- Pipeline monitoring
- Data quality engineering
Customer Presentation & Consulting Capabilities
The architect must demonstrate:
- Executive-level architecture storytelling
- Whiteboarding complex data ecosystems
- ROI/TCO discussions
- Transformation roadmap articulation
- Architecture review leadership
- Workshop facilitation
- Proposal & solution positioning
- Stakeholder influence across technical and business teams
Leadership & Delivery Expectations
- Lead distributed architecture teams
- Governance & escalation authority
- Engineering excellence advocacy
- Cross-functional collaboration
- Risk management leadership
Qualifications
- Bachelors/Masters in Computer Science/Applications or related discipline
- 18+ years in enterprise data engineering/cloud architecture
- Proven leadership in modernization programs
- Consulting/product engineering exposure preferred
Must Have/Preferred Certifications
- AWS Cloud Architect Professional
- Azure Data Engineer/Architect
- Google Professional Data Engineer
- Lakehouse platform certifications
- AI/ML cloud certifications
Key Competencies
- Strategic architectural thinking
- Executive communication
- Systems-level problem solving
- Business-technology alignment
- Leadership under ambiguity
Senior Data Architect Evaluation Framework
Evaluation Weightage Model
Enterprise Data Architecture Depth: 30%
Evaluate ability to:
- Design large-scale cloud-native data platforms
- Modernize legacy warehouses to lakehouse ecosystems
- Architect streaming + batch systems
- Apply advanced data modeling principles (hands-on)
- Optimize performance and scalability
Cloud Platform Mastery 20%
Assess:
- Multi-cloud architecture capability
- Service selection and optimization
- Infrastructure automation
- Security integration
- Cost governance
Customer Advisory & Executive Communication 30%
Measure:
- Architecture storytelling
- Workshop facilitation
- Business translation skills
- Stakeholder influence
- Presentation clarity
Delivery Governance & Engineering Leadership 10%
Evaluate:
- Large program oversight
- Risk mitigation
- Quality frameworks
- Agile/DevOps alignment
- Team leadership
AI/ML & Advanced Analytics Integration 10%
Assess:
- ML-ready data pipeline design
- Feature engineering architecture
- GenAI ecosystem awareness
Why Innominds?
A note for those who want more than just a job
ISV-First DNA
Innominds was purpose-built for the ISV ecosystem. We speak the language of product engineering, SaaS velocity, and platform scale not generic IT services.
Global Reach, Startup Soul
With 2,500+ engineers across the US, India, and EMEA, we combine enterprise delivery muscle with the agility and ownership culture of a growth-stage company.
Cutting-Edge Work
From AI/ML and cloud-native platforms to embedded security and digital transformation ,you will work on whats next, not whats left.
Real Career Ownership
We believe in promoting from within. Your KPIs here translate into a growth trajectory Delivery Director, AVP, or beyond. You own your arc.
Customer Intimacy at Scale
You will have direct access to senior customer leadership not filtered through layers. Real relationships, real impact, real accountability.
Competitive Rewards
Market-leading compensation, performance bonuses.
If you believe delivery is an art and customer trust is currency, Innominds is your stage.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: OtherRole Category: OtherRole: OtherEmployement Type: Full timeContact Details:
Company: Innominds SoftwareLocation(s): Hyderabad
Keyskills: Azure Data Architecture ETL GCP Data Modeling AWS Cloud Native
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