Data Architect

Roles & Responsibilities :
Job Summary: We are seeking an experienced Data Architect & Engineer to contribute to our core data initiatives. This role involves active participation in designing building and optimizing our enterprise data platforms covering strategic data architecture and modeling advanced data engineering robust data archiving and innovative applications of AI/Generative AI. You will contribute to the technical vision for our enterprise data landscape ensuring scalability reliability compliance and leveraging data as a strategic asset.

Key Responsibilities:

Enterprise Data Architecture & Modeling:

Contribute to the design and evolution of enterprise-wide data models (conceptual logical physical) for data warehousing data lakes streaming and operational stores.

Help define and enforce enterprise data architecture standards and best practices.

Evaluate recommend and implement new data technologies and platforms.

AI/Generative AI Integration:

Explore prototype and implement strategic applications of AI and GenAI (LLMs RAG vector databases) for data analysis automation and intelligent decision-making.

Evaluate and integrate AI tools and platforms to embed AI capabilities into our data products.

Cloud & DevOps for Data:

Design and deploy scalable cost-effective data solutions on cloud platforms (AWS Azure) adhering to DevOps principles (CI/CD infrastructure as code).

Ensure data security access control and compliance across cloud-native data environments.

Advanced Data Engineering & Pipelines:

Design and implement scalable resilient and performant data pipelines for high-volume data ingestion transformation and delivery.

Develop robust ETL/ELT processes leveraging modern cloud-native services and microservices principles.

Contribute to metadata management data lineage and data quality frameworks.

Enterprise Data Archiving & Application Retirement:

Participate in the technical execution of our data archiving strategy for diverse enterprise applications including source system analysis (custom apps legacy COTS databases).

Design and implement efficient data transfer strategies and ETL using advanced tools (including JiVS with provided training) for archiving.

Ensure archived data is transformed and accessible for statutory audit and historical reporting optimizing for future analytics.

Contribute to implementing and managing robust data retention policies and data lifecycle management within the archiving platform.

Data Governance & Technical Contribution:

Actively contribute to defining and implementing enterprise data governance policies master data management (MDM) and data security frameworks.

Provide technical expertise architectural guidance and mentorship within cross-functional teams.

Qualifications :

Educational qualification:

BTech/Mtech/MCA or Equivalent

Experience :

• Experience: 10-12 years of progressive experience in software architecture data architecture or data engineering with a proven track record of significant technical contributions.

• Architectural & Design Skills: Strong understanding and practical experience in designing and implementing scalable resilient and performant software architectures (e.g. Microservices Event-Driven Cloud-Native).

• Data Engineering Fundamentals: Deep expertise in data modeling ETL/ELT principles data integration patterns data quality and metadata management.

• Programming & Scripting: Expert proficiency in Java/Spring Boot is highly desirable. Strong command of scripting languages (e.g. Python Shell Scripting) for data manipulation and automation.

• Database & Data Stores: Extensive experience with relational databases (e.g. PostgreSQL Oracle SQL Server IBM DB2) and NoSQL databases (e.g. MongoDB Neo4J Vector Databases). Advanced SQL.

• Cloud & DevOps: Hands-on experience with major cloud platforms (AWS Azure) and services. Strong understanding of Docker Kubernetes CI/CD and monitoring tools (Prometheus Grafana).

• Enterprise Application Integration: Proven experience in analyzing extracting data from and integrating with diverse enterprise applications and legacy systems.

• AI/Generative AI: Practical experience with LLMs Retrieval-Augmented Generation (RAG) vector databases or frameworks like Spring AI.

• Analytical & Problem-Solving: Exceptional analytical problem-solving and troubleshooting skills.

• Communication & Collaboration: Excellent verbal and written communication skills in English. Proven ability to collaborate effectively provide technical guidance and contribute within cross-functional teams.

Mandatory/requires Skills :
As per above

Preferred Skills :
As per above

Remote Work :

No

Employment Type :

Full-time