Lead Consultant

Job Description Inviting applications for the role of Lead Consultant- Sr. Snowflake Data Engineer (Python+Cloud) In this role, the Sr. Snowflake Data Engineer is responsible for providing technical direction and lead a group of one or more developer to address a goal. Job Description: - Design, develop, and optimize scalable data pipelines on Snowflake using Python and cloud-native tools. - Build interactive, user-friendly data applications and dashboards using Streamlit to visualize AI-driven insights. - Develop Python-based user-defined functions (UDFs) and stored procedures to enhance Snowflake processing. - Integrate generative AI models and Cortex AI capabilities into the Snowflake ecosystem to deliver intelligent, automated data products. - Implement real-time data streaming and ingestion pipelines to support AI workloads and analytics. - Leverage cloud platforms (AWS, Azure, GCP) for scalable data processing and seamless Snowflake integration. - Optimize Snowflake data models and query performance to support complex AI inference workloads. - Lead efforts in automating data workflows and model deployment pipelines with Python scripting and orchestration tools. - Ensure data governance, security, and role-based access control (RBAC) compliance across Snowflake deployments. - Develop prototypes and proof-of-concept applications leveraging Gen AI to demonstrate business value. - Collaborate with stakeholders to translate AI use cases into scalable data engineering solutions. - Mentor and lead engineering teams on best practices around Python, Snowflake, Gen AI, and Streamlit development. - Stay current with evolving AI technologies, Snowflake enhancements, and cloud data engineering trends to drive innovation. - Snowflake SnowPro Core Certification is must. Roles and Responsibilities: - Requirement gathering, creating design document, providing solutions to customer, work with offshore team etc. - Writing SQL queries against Snowflake, developing scripts to do Extract, Load, and Transform data. - Hands-on experience with Snowflake utilities such as SnowSQL, Bulk copy, Snowpipe, Tasks, Streams, Time travel, Cloning, Optimizer, Metadata Manager, data sharing, stored procedures and UDFs, Snowsight. - Have experience with Snowflake cloud data warehouse and AWS S3 bucket or Azure blob storage container for integrating data from multiple source system. - Should have have some exp on AWS services (S3, Glue, Lambda) or Azure services ( Blob Storage, ADLS gen2, ADF) - Should have good experience in Python/Pyspark.integration with Snowflake and cloud (AWS/Azure) with ability to leverage cloud services for data processing and storage. - Proficiency in Python programming language, including knowledge of data types, variables, functions, loops, conditionals, and other Python-specific concepts. - Knowledge of ETL (Extract, Transform, Load) processes and tools, and ability to design and develop efficient ETL jobs using Python and Pyspark. - Should have some experience on Snowflake RBAC and data security. - Should have good experience in implementing CDC or SCD type-2. - Should have good experience in implementing Snowflake Best Practices - In-depth understanding of Data Warehouse, ETL concepts and Data Modelling - Experience in requirement gathering, analysis, designing, development, and deployment. - Should Have experience building data ingestion pipeline - Optimize and tune data pipelines for performance and scalability - Able to communicate with clients and lead team. - Proficiency in working with Airflow or other workflow management tools for scheduling and managing ETL jobs. - Good to have experience in deployment using CI/CD tools and exp in repositories like Azure repo ,Github etc. Qualifications we seek in you Minimum qualifications - B.E./ Masters in Computer Science, Information technology, or Computer engineering or any equivalent degree with good IT experience and relevant as Senior Snowflake Data Engineer. - Skill Metrix: - Snowflake, Cortex AI, Python/PySpark, AWS/Azure, ETL concepts, Data Modeling & Data Warehousing concepts - - - - - -