Data Architect

Location: Noida, NCR, India

Experience: 3–5 Years

Employment Type: Full-Time

About Klypup

Klypup is an AWS Partner and a digitally native cloud services company helping businesses leverage the power of AI and AWS. We help enterprises modernize their technology landscapes by building scalable, secure, and high-performance cloud-native solutions.

As businesses increasingly adopt AI and advanced analytics, we focus on building the cloud infrastructure and data foundations that make these technologies practical, reliable, and scalable.

As a Specialist Solutions Architect – Data (AI & Cloud Solutions), you’ll work on designing modern data platforms, building optimized data pipelines, and leading cloud data migrations. You’ll help bridge the gap between enterprise data and next-generation AI systems, enabling businesses to unlock greater value from their data.

Your Mission

AWS & Multi-Platform Data Architecture

• Design and implement scalable, secure, and cost-effective data architectures on Amazon Web Services (AWS).
• Build and optimize data warehouses, data lakes, and lakehouse architectures.
• Work with AWS services such as Amazon S3, Redshift, Aurora, RDS, DynamoDB, and AWS Glue.
• Integrate modern data platforms, including Databricks, Snowflake, and MongoDB.
• Develop cloud-native data solutions that support enterprise analytics, reporting, and AI workloads.
• Evaluate architectural approaches based on performance, scalability, security, and cost.
• Create reusable architecture patterns and best practices for cloud data solutions.

AI-Ready Data Pipelines & Engineering

• Design, develop, and optimize ETL/ELT pipelines for analytics, machine learning, and generative AI workloads.
• Build data ingestion and transformation workflows to process structured and unstructured data.
• Collaborate with Data Scientists and ML Engineers to prepare data for model training, inference, and AI applications.
• Develop data foundations that support Retrieval-Augmented Generation (RAG) and AI-powered insight generation.
• Explore vector databases, semantic search, and natural-language data exploration.
• Structure enterprise data to enable AI agents to retrieve, process, and analyze relevant information.
• Improve data pipeline reliability, scalability, and processing efficiency.

Cloud Data Migration & Integration

• Plan and execute end-to-end data migrations from legacy systems and disparate data sources to modern cloud environments.
• Assess existing data infrastructure and develop migration strategies aligned with business requirements.
• Design migration workflows that minimize downtime and reduce operational disruption.
• Validate data integrity, consistency, and completeness throughout the migration process.
• Integrate databases, data warehouses, and external platforms with AWS-based data architectures.
• Identify and resolve migration challenges related to compatibility, performance, and data quality.
• Support post-migration optimization, monitoring, and ongoing maintenance.

Modern Data Strategies & AI Enablement

• Design modern data architectures using lakehouse principles and open table formats such as Apache Iceberg.
• Explore zero-ETL integrations and approaches that simplify data movement between systems.
• Build semantic search foundations that enable AI applications to retrieve relevant enterprise information.
• Support the development of agent-ready data platforms for AI-driven search, analysis, and decision-making.
• Evaluate emerging data technologies and determine their practical applications in enterprise environments.
• Help organizations transition from traditional business intelligence and data warehousing to AI-enabled data platforms.

Data Governance, Quality & Security

• Establish data governance practices covering data quality, lineage, metadata, and lifecycle management.
• Implement data validation and quality checks across ingestion and transformation pipelines.
• Ensure data consistency, traceability, and reliability across platforms.
• Apply appropriate security controls and access management to cloud data environments.
• Support compliance requirements and responsible data management practices.
• Identify and address data quality issues that could affect analytics or AI-generated outputs.

Performance Optimization & Cost Management

• Monitor and optimize query performance, storage utilization, and data pipeline throughput.
• Improve the efficiency of data processing and transformation workflows.
• Evaluate storage tiers and compute configurations to manage cloud infrastructure costs.
• Troubleshoot bottlenecks across databases, data warehouses, and distributed processing systems.
• Recommend improvements to enhance system reliability, scalability, and operational efficiency.

Technical Leadership & Customer Enablement

• Participate in customer discussions to understand business requirements and translate them into technical solutions.
• Lead technical workshops, architecture reviews, and Proofs of Concept (PoCs).
• Develop reference architectures, technical documentation, and reusable implementation assets.
• Explain architectural decisions, trade-offs, and recommendations to technical and business stakeholders.
• Collaborate with cloud engineers, data engineers, Data Scientists, and ML Engineers.
• Support customers in adopting modern AWS data and AI solutions.

What We’re Looking For

Experience

• 3–5 years of professional experience in data engineering, data warehousing, or data architecture.
• Hands-on experience designing, building, and maintaining cloud-based data solutions.
• Demonstrated experience implementing data pipelines and integrating multiple data sources.
• Practical experience working with AWS data services and modern data platforms.
• Experience delivering or contributing to cloud data migration projects.
• Understanding of enterprise data architecture, data modeling, and data lifecycle management.
• Ability to translate business requirements into scalable technical solutions.

Core Technical Skills

• AWS: Hands-on experience with Amazon S3, Redshift, Aurora, RDS, DynamoDB, AWS Glue, and related data services.
• Data Platforms: Experience integrating or working with Databricks, Snowflake, and MongoDB.
• Programming: Strong proficiency in Python and SQL.
• Data Engineering: Practical knowledge of ETL/ELT development, data ingestion, transformation, and pipeline optimization.
• Databases & Warehousing: Understanding of relational databases, NoSQL databases, data warehouses, data lakes, and lakehouse architectures.
• Workflow Orchestration: Familiarity with Apache Airflow or similar orchestration tools.
• Cloud Architecture: Understanding of scalability, availability, security, performance, and cost optimization.
• Data Integration: Experience connecting databases, applications, APIs, and external data sources.

AI & Modern Data Skills

• Understanding of generative AI and AI-enabled data applications.
• Familiarity with Retrieval-Augmented Generation (RAG) architectures.
• Understanding of vector databases, embeddings, and semantic search.
• Knowledge of preparing structured and unstructured data for AI workloads.
• Familiarity with agent-ready data architectures and AI-driven information retrieval.
• Awareness of modern data approaches such as Apache Iceberg, lakehouse architectures, and zero-ETL integrations.

Architecture & Problem-Solving Skills

• Ability to design scalable and maintainable data architectures.
• Strong analytical thinking and troubleshooting skills.
• Ability to evaluate technical trade-offs and recommend suitable solutions.
• Understanding of data governance, data quality, metadata, and lineage.
• Ability to optimize infrastructure performance and cloud expenditure.
• Strong documentation and technical communication skills.
• Ability to work independently while collaborating effectively across teams.

Preferred Experience

• Experience building data foundations for generative AI, RAG, or AI agent applications.
• Hands-on experience with Apache Iceberg or other open table formats.
• Familiarity with zero-ETL integrations and modern cloud data strategies.
• Experience with vector databases and semantic retrieval systems.
• Experience working directly with enterprise customers on architecture and migration projects.
• Familiarity with AWS cloud security, identity and access management, and governance practices.
• Experience developing technical Proofs of Concept and reusable reference architectures.

Certifications

The following certifications are strongly preferred:

• AWS Certified Solutions Architect.
• AWS Certified Data Engineer – Associate.
• Relevant AWS analytics, database, or machine learning certifications.
• Relevant certifications in Databricks or Snowflake.

Application Requirements

Please include:

• An updated resume highlighting relevant data engineering and cloud architecture experience.
• Details of AWS data services and modern data platforms you have worked with.
• Examples of data pipelines, cloud data architectures, or migration projects you have contributed to.
• A brief description of your role, technical contributions, and the challenges you solved.
• Details of any relevant AWS, Databricks, or Snowflake certifications.
• Links to relevant technical projects, portfolios, or professional profiles, where available.

What We Offer

Career & Growth

• Work on real-world AWS, cloud data, and AI initiatives.
• Gain exposure to modern enterprise data architectures and AI-ready platforms.
• Collaborate with technical leaders and cross-functional engineering teams.
• Take ownership of architecture decisions and implementation challenges.
• Develop expertise in cloud modernization, data engineering, and AI enablement.
• Build a strong track record in applied cloud and data solutions.

Learning & Innovation

• Work with AWS and modern data technologies such as Databricks, Snowflake, and MongoDB.
• Explore emerging approaches in generative AI, RAG, and agent-ready data platforms.
• Gain hands-on experience with modern data architectures and cloud migration strategies.
• Evaluate new technologies and apply them to practical business problems.
• Develop your technical expertise through continuous learning and experimentation.

Culture

• Founder-led, fast-paced startup environment.
• Opportunities to work on challenging customer and product requirements.
• A culture that values technical ownership, practical problem-solving, and continuous improvement.
• Collaboration across cloud, data, and AI engineering disciplines.
• Freedom to explore innovative approaches to enterprise technology challenges.

Bonus Points

We’d love to see candidates with:

• AWS certifications relevant to architecture, data engineering, or analytics.
• Experience designing AWS-native data platforms.
• Experience with Databricks, Snowflake, and MongoDB integrations.
• Successful delivery of cloud data migrations.
• Experience optimizing large-scale data pipelines and warehouse performance.
• Practical knowledge of RAG, vector search, and AI-ready data architectures.
• Experience with Apache Iceberg, lakehouse architecture, or zero-ETL patterns.
• Strong customer-facing communication and technical workshop experience.

Success Milestones

First 3 Months

• Understand Klypup’s cloud, data, and AI delivery practices.
• Become familiar with existing architecture patterns and customer requirements.
• Contribute to AWS data architecture, pipeline development, and technical assessments.
• Demonstrate proficiency in core AWS data services and data engineering practices.

First 6 Months

• Independently deliver defined data engineering and architecture components.
• Contribute to cloud data migration and platform integration projects.
• Build or optimize data pipelines supporting analytics and AI workloads.
• Participate in architecture reviews, technical workshops, and Proofs of Concept.

First 12 Months

• Take ownership of significant data architecture and migration workstreams.
• Deliver scalable, secure, and cost-effective cloud data solutions.
• Contribute to AI-ready data platforms, RAG foundations, and modern data strategies where applicable.
• Develop reusable reference architectures and help improve Klypup’s cloud data delivery practices.

Klypup is an Equal Opportunity Employer.

Build modern data platforms. Power the next generation of AI.