Data Engineer

Responsibilities:

Data Engineering & Snowflake

•        Design, develop, and maintain scalable data pipelines using Snowflake
•        Implement efficient ELT/ETL processes for structured and semi-structured data
•        Optimize Snowflake performance (clustering, partitioning, query tuning, cost optimization)
•        Manage data ingestion using tools like Snowpipe, Streams, and Tasks

Cloud & Architecture

•        Build and manage data solutions on Amazon Web Services / Azure / GCP
•        Design modern data architectures (Data Lake, Lakehouse, Data Warehouse)
•        Ensure scalability, reliability, and security of data platforms

Data Science & Machine Learning Enablement

•        Collaborate with data scientists to support model development and deployment
•        Build and maintain feature engineering pipelines for ML models
•        Enable data availability for training, validation, and inference workflows
•        Understand and apply key ML concepts (supervised/unsupervised learning, model evaluation, bias/variance)

Advanced Analytics & Modeling

•        Design and maintain data models for analytics and ML use cases
•        Work with large-scale datasets using SQL, Python, and distributed computing frameworks
•        Support real-time and batch data processing

Data Science & Machine Learning Enablement

•        Collaborate with data scientists to support model development and deployment
•        Build and maintain feature engineering pipelines for ML models
•        Enable data availability for training, validation, and inference workflows
•        Understand and apply key ML concepts (supervised/unsupervised learning, model evaluation, bias/variance)

Advanced Analytics & Modeling

•        Design and maintain data models for analytics and ML use cases
•        Work with large-scale datasets using SQL, Python, and distributed computing frameworks
•        Support real-time and batch data processing

Required Skills:

•        Python
•        Snowflake
•        Data Science
•        Machine Learning
•        ETL
•        ML concepts (supervised/unsupervised learning, model evaluation, bias/variance)

Posted By