Cloud and Data Architect

10 hours ago

Hinjawadi Pune Maharashtra, India BirlaSoft Full time

Country/Region: IN
- Requisition ID: 21248
- Work Model:

- Position Type:

- Salary Range:

- Location: INDIA - PUNE - BIRLASOFT OFFICE - HINJAWADI

**Title**:Cloud and Data Architect**:

- Description:

**Area(s) of responsibility**:
**Senior/Lead Cloud and Data Architect**

**Position Overview**

**Key Responsibilities**

**Leadership and Collaboration**:

- Lead and inspire cross-functional teams, including **data engineers**, **data scientists**, **ML engineers**, and **BI analysts**, fostering a culture of collaboration, innovation, and continuous improvement.
- Act as the central technical point of contact for enterprise-wide data and analytics initiatives, ensuring alignment with business objectives.

**Enterprise Architecture**:

- Develop and maintain enterprise-level architectural blueprints for cloud and data platforms, ensuring interoperability and scalability.
- Create a cohesive architecture that integrates **data platforms**, **ML systems**, and **business intelligence** tools while adhering to governance and compliance requirements.
- Provide strategic input on technology roadmaps, ensuring alignment with organizational vision and future-proofing investments.
- Evaluate and recommend emerging technologies and frameworks to enhance enterprise data capabilities.

**Cloud and Data Platform Engineering**:

- Design and implement cutting-edge cloud architectures using **AWS**, **Azure**, or **GCP**, focusing on scalability, security, and cost optimization.
- Build and optimize modern data platforms using **Databricks** or **Snowflake** for advanced analytics and real-time data processing.
- Lead the development of end-to-end data pipelines, ensuring robust integration between data sources, platforms, and analytics tools.

**Data and ML Integration**:

- Collaborate with ML teams to deploy machine learning pipelines and operationalize AI models within enterprise systems.
- Provide architectural support for ML initiatives, including data preparation, feature engineering, and model lifecycle management.
- Enable seamless integration of ML and analytics into business workflows, enhancing decision-making and operational efficiency.

**Data Warehousing and Modeling**:

- Architect enterprise data warehouses with robust data models, ensuring high performance and reliability for analytics workloads.
- Lead the development of advanced data models that cater to both analytical and operational requirements, emphasizing scalability and data quality.

**DevOps and DataOps Practices**:

- Establish and enforce **DevOps** and **DataOps** pipelines to automate deployments, enhance agility, and ensure operational excellence.

**Governance, Security, and Compliance**:

- Implement robust security measures to protect sensitive data across platforms, adhering to privacy standards such as GDPR and CCPA.

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