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.
Requirements
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).
