Principal Data Engineer

Job Title: Principal Data Engineer

Experience: 12+ Years 
Location: Bengaluru 
Notice Period: Immediate Joiner or max 15days preferred

About the Role

We are seeking an experienced Principal Data Engineer  with 12+ years of experience in designing and delivering enterprise-scale cloud data platforms. The ideal candidate will have deep expertise in Snowflake, data architecture, data modeling, and modern ETL/ELT frameworks, with a proven track record of building highly scalable, secure, and high-performance data ecosystems.

This role requires strong technical leadership to define enterprise data architecture, establish engineering best practices, mentor engineering teams, and collaborate with business stakeholders to deliver robust data solutions that support analytics, AI/ML, and business intelligence initiatives.

Key Responsibilities

• Design and architect enterprise-scale data platforms using Snowflake.
  

• Lead the architecture, development, and optimization of scalable ETL/ELT data pipelines.
  

• Define enterprise data architecture standards, governance frameworks, and best practices.
  

• Design and implement modern cloud-based data warehouses and lakehouse architectures.
  

• Develop scalable dimensional, relational, and data vault models based on business requirements.
  

• Build reusable and metadata-driven data engineering frameworks.
  

• Optimize Snowflake performance through warehouse sizing, clustering, partitioning, query tuning, and cost optimization.
  

• Lead migration of legacy data warehouses to Snowflake.
  

• Design secure and governed data platforms incorporating RBAC, masking policies, row-level security, and data lineage.
  

• Architect workflow orchestration using Apache Airflow and dbt.
  

• Collaborate with enterprise architects, data scientists, BI teams, product owners, and business stakeholders to define long-term data strategy.
  

• Drive cloud-native architecture across AWS, Azure, or GCP environments.
  

• Implement CI/CD pipelines, DevOps practices, infrastructure automation, and monitoring for data platforms.
  

• Establish data quality, observability, metadata management, and master data management standards.
  

• Review architecture, conduct design reviews, and provide technical leadership across multiple projects.
  

• Mentor senior engineers and lead technical decision-making across cross-functional teams.
  

• Stay updated on emerging technologies and recommend improvements to enterprise data platforms.
  

Preferred Skills

• Experience with Data Lake and Lakehouse architectures.
  

• Knowledge of streaming technologies such as Kafka or Event Hubs.
  

• Experience with Databricks or similar distributed data processing platforms.
  

• Familiarity with Infrastructure as Code (Terraform, CloudFormation, ARM/Bicep).
  

• Experience integrating enterprise BI tools such as Power BI, Tableau, or Looker.
  

• Exposure to AI/ML data engineering pipelines.
  

• Experience implementing enterprise data governance frameworks using tools such as Microsoft Purview, Collibra, or Alation.
  

• Snowflake certifications are highly desirable.