Job Summary

We are seeking an experienced Databricks, Snowflake and PySpark Architect to design, architect, and deliver scalable cloud-based data platforms and modern analytics solutions. The ideal candidate will have deep expertise in data architecture, cloud-native technologies, data engineering best practices, and large-scale data transformation programs. The role involves collaborating with business stakeholders, solution architects, and engineering teams to build high-performance, secure, and cost-efficient data ecosystems leveraging Databricks, Snowflake, and PySpark.

Responsibilities

- Define and drive enterprise-scale data architecture and modernization strategies.
- Design end-to-end cloud data platforms using Databricks, Snowflake, and contemporary data engineering frameworks.
- Architect scalable batch and real-time data processing pipelines for large datasets.
- Lead data lake, lakehouse, and data warehouse implementations.
- Provide architectural guidance on performance optimization, scalability, governance, security, and reliability.
- Work closely with business stakeholders to understand analytical and reporting requirements.
- Review technical designs, conduct architecture assessments, and establish best practices.
- Mentor data engineers and technical leads across project engagements.
- Lead technology evaluations, proof-of-concepts (POCs), and solution accelerators.
- Drive cloud migration and modernization initiatives from legacy data platforms.
- Ensure adherence to enterprise security, compliance, and data governance standards.




- Collaborate with DevOps and platform teams to implement CI/CD and infrastructure automation for data solutions.

Technical Requirements

- 11+ years of experience in Data Engineering, Analytics, or Data Platform Architecture.
- Strong expertise in Databricks Lakehouse Platform.
- Extensive hands-on experience with Snowflake Data Cloud.
- Expert-level proficiency in PySpark and distributed data processing.
- Strong experience in designing enterprise-scale data lakes, data warehouses, and lakehouse architectures.
- Hands-on experience with cloud platforms such as Azure (ADF, ADLS Gen2, Synapse, Azure Databricks); AWS (S3, Glue, EMR, Redshift); GCP (BigQuery, DataProc, Cloud Storage).
- Strong SQL and data modeling skills (Dimensional Modeling, Data Vault, Star Schema).
- Experience building ETL/ELT frameworks and reusable data engineering accelerators.
- Knowledge of Data Governance, Metadata Management, Data Lineage, and Data Quality frameworks.
- Experience with orchestration tools such as Airflow, Azure Data Factory, or similar.
- Expertise in performance tuning and cost optimization within Databricks and Snowflake environments.
- Experience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or similar tools.

Additional Responsibilities

- Enterprise Data Architecture
- Solution Design Governance
- Technology Strategy Roadmap
- Presales Client Advisory
- Architecture Reviews
- Data Platform Modernization
- Technical Leadership Mentoring
- Cost Optimization Non-Functional Requirements
- Stakeholder Engagement

Educational Requirement

Bachelor of Engineering

Preferred Skills

- Technology->Data Engineering->Databricks

Service Line

Data Analytics Unit

📌 Databricks Architect (Bengaluru)
🏢 Infosys
📍 Bengaluru