• Data Pipeline Development: Build and maintain efficient, scalable, and reliable  pipelines to support analytics and reporting workloads. 
• Data Integration: Implement seamless integration across diverse data sources  (structured, semi-structured, and unstructured) into Snowflake and AWS-based  data platforms (Postgres/Aurora Postgres/Dynamo DB)  
• Data Governance: Establish and enforce data governance frameworks including  metadata management, data lineage, data quality and data entitlement.  
• Cloud Engineering: Leverage AWS services (e.g., S3, Glue, Lambda, Redshift,  EMR) to design cloud-native data solutions.  
• Performance Optimization: Monitor, troubleshoot and optimize data workflows  for speed, scalability, and cost efficiency. 
• Collaboration: Partner with data architects, analysts and business stakeholders  to translate requirements into technical solutions.  
• Best Practices: Drive adoption of engineering best practices, including CI/CD,  automation, and Infrastructure-as-Code for data platforms.  
• Leveraging AI: Any experience in leveraging AI in execution or implementation of  data pipelines and data integration solutions.  

•  10–12 years of hands-on experience in data engineering.  
• Strong hands-on expertise in Snowflake data modelling, performance tuning,  CDC, security and governance (Snowpipe, Dynamic Tables, DBT, Streams,  RBAC).  
• Hands-on experience with AWS services (S3, Glue, Postgres, Redshift, EMR,  IAM).  
• Proficiency in RDBMS concepts, SQL and programming languages such as  Python or Scala.  
• Experience with ETL/ELT frameworks and workflow orchestration tools (  Snaplogic, Airflow, DBT).  
• Good understanding of data governance principles and implementation  experience  
• Familiarity with DevOps practices and CI/CD pipelines for data engineering.  
• Excellent problem-solving, communication and stakeholder management skills.  

Preferred Qualifications  

• Exposure to big data technologies (Spark, Hadoop).  
• Experience with real-time data streaming (Kafka, Kinesis).  
• Knowledge of data cataloging tools (Collibra, Alation).  
• Experience of using  AI tools (Cloude, Cortex)   
• Prior experience in implementing enterprise-scale data modernization initiatives.