Data Architect

Job Description

Roles & Responsibilities :
Job Summary:We are seeking an experiencedData Architect & Engineerto 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 fordiverse 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 inJava/Spring Bootis 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