About the Role :

We are seeking a highly experienced Senior Data Architect to lead the design, development, and implementation of modern, scalable, and high-performing enterprise data platforms. The ideal candidate will have deep expertise in data architecture, governance, modeling, and cloud-based data solutions, with the ability to define strategy and drive large-scale data initiatives across hybrid and multi-cloud environments.

Key Responsibilities (KRA) :

- Design, implement, and optimize enterprise-grade data architectures for large-scale, hybrid, and multi-cloud environments.

- Develop and maintain data models, data pipelines, and data integration frameworks supporting analytical and operational needs.

- Define and enforce data governance, quality, and security standards across all data systems.

- Collaborate with business stakeholders and engineering teams to translate business requirements into scalable data solutions.

- Lead architecture reviews, technology evaluations, and platform selection for data management and analytics.

- Optimize data storage, processing, and retrieval strategies for high performance and cost efficiency.

- Mentor and guide engineering teams on best practices in data engineering, modeling, and architecture design.

- Implement ETL/ELT frameworks, integrating data from multiple structured and unstructured sources.

- Drive adoption of modern data technologies such as Spark, Snowflake, Databricks, BigQuery, and other cloud data warehouses.

- Collaborate with DevOps and cloud teams to implement IaC, CI/CD pipelines, and automated deployment of data infrastructure.

- Ensure data compliance, auditability, and lineage tracking in line with enterprise governance policies.

- Stay current with emerging trends and technologies to continuously improve the data ecosystem.

Key Skillsets :

- Strong experience in Modern Data Architecture and Enterprise Data Platforms.

- Expertise in RDBMS, NoSQL, ETL, and Data Warehousing.

- Proficiency in Data Governance, Data Modeling, and Performance Optimization.

- Hands-on experience with Azure, AWS, or GCP cloud platforms.

- Strong command over Python, SQL, and Spark for large-scale data processing.

- Working knowledge of Cloud Data Warehouses such as Snowflake, BigQuery, or Databricks.

- Familiarity with Power BI, Tableau, or Looker for data visualization (good to have).

- Experience in Infrastructure as Code (IaC) and CI/CD for data pipelines (good to have).