Snowflake Data Engineer (Snowflake- AWS- SQL- Python)

Urgency / TAT: Urgent / within 30 days

Duration: Long Term

Working Hours: 2pm to 11pm IST

Band: U3 or U4

Working Location: Remote - PAN INDIA (preferably near to Tech M office)

Experience: 6-12 Years

Key words to search for the candidate.

Mandatory – Snowflake, SQL, Python – Pandas, Data Modelling

Good to have but not mandatory – Healthcare Domain experience, AWS S3 & Blob

Certifications (Good to have) – None

Job Description

Required Skills and Qualifications

inSnowflake, with extensive experience in data warehouse architecture, management and Snowpipe.

of User Defined Function vs Stored Procedure, Multi Clusters DWH, Views vs Secure Views & Materialized views in Snowflake.

tuning and how to improve performance in Snowflake.

inSQL and database development, with the ability to write complex queries and optimize database performance.

programming skills inPython, including experience with data libraries and frameworks, like Pandas.

understanding ofdata modeling principles and best practices.

communication and collaboration skills, with the ability to work in a team environment.

or Master’s degree in computer science, Information Technology, or a related field.

Key Responsibilities

and implement robust, scalable data pipelines and architectures in Snowflake, optimizing data flow and collection for cross functional teams.

complex SQL queries and scripts to support data transformation, aggregation, and analysis.

Python for automating data processes, integrating data systems, and building advanced analytics models.

data modeling initiatives, ensuring the integrity and efficiency of data structures.

with cross functional teams to understand data needs, gather requirements, and deliver data driven solutions that align with business goals.

data security and compliance measures, adhering to industry standards and regulations.

data analysis and provide insights to support decision making and strategy development.

abreast of emerging technologies and trends in data engineering, recommending, and implementing improvements to our data systems and processes.