Key Responsibilities

1. Design and implement enterprise-scale data Lakehouse solutions using Teradata, Talend and Qlik.

2. Drive architectural decisions aligned with RFP requirements and organizational strategy.

3. Provide technical leadership and mentorship to development teams and work with technical leads to guide parallelized delivery across multiple tracks on a very tight timeline.

4. Lead solution design and optimization initiatives.

5. Participate in detailed project planning with the program leads to ensure that tight deadlines are met.

6. Lead technical discussions with clients technical and functional leadership as well as consulting partners to present the technical state of the program.

Technical Requirements

1. Extensive (at least 10+ yrs) relevant experience in design and development of data warehouse, data lake and data Lakehouse architectures.

2. Experience in greenfield data Lakehouse implementations in an on-premise environment including familiarity with workplace sizing, system integrations.

3. Functional expertise in working for insurance domain is an added advantage.

4. Proven experience in data modelling including modelling for raw, validated and enriched layers of a data Lakehouse using medallion architecture principles.

5. Strong experience of on-premise deployment using Teradata and Talend.



Deep understanding of Teradata architecture and Talend capabilities is very important to drive the program.

6. Proven experience in data quality and governance is a must. This includes experience in setting up end-to-end data quality checks, data stewardship processes, data catalog, data security and access control, data lineage etc.

7. Experience in integrating with data access management solutions.

8. Ability to drive solutions for large-scale data migrations with quality checks and reconciliation.

9. Expertise in setting up data platform monitoring tools for monitoring the health and NFRs across data platform including the repository (Teradata), data processing (Talend) and data consumption (Qlik, AI/ML models, data services).

10. Understanding of digital event-based architectures for supporting near-real time processing and data services (APIs) will provide a valuable edge.

11. Proficiency in data security implementation

12. Experience in integration architecture.

13. Understanding of data analytics including BI reporting, AI/ML model development, and data services.

14. Drive setup and adoption of DevOps, MLOps tools and processes.

15. Ability to document in detail about the architecture and low-level design for various components in the solution.

📌 Data Architect (Mumbai)
🏢 Intellics Global Services Mumbai
📍 Mumbai