Snowflake Data Engineer - Remote

Title: Snowflake Data Engineer

Location: Remote

Must have: Python, SQL, PySpark, snowflake

Job Description

We are seeking an experienced Snowflake Data Engineer with strong hands-on expertise in Python, SQL, PySpark, and Snowflake to design, develop, and maintain scalable data pipelines and cloud-based data solutions.

Responsibilities

Design, develop, and optimize ETL/ELT data pipelines using Python and PySpark.
Develop complex SQL queries, stored procedures, views, and data transformations.
Build and maintain scalable data solutions using Snowflake.
Develop data ingestion and transformation pipelines from multiple structured and semi-structured sources.
Perform data modeling, data integration, data validation, and data quality activities.
Optimize Snowflake queries, warehouses, tables, and workloads for performance and cost.
Develop PySpark applications for large-scale data processing and transformation.
Implement data cleansing, enrichment, aggregation, and transformation processes using Python and SQL.
Troubleshoot data pipeline failures and resolve data quality and performance issues.
Collaborate with Data Architects, Analysts, BI teams, and business stakeholders to understand data requirements.
Implement monitoring, logging, error handling, and operational support for production data pipelines.
Follow coding, testing, documentation, and deployment best practices.

Required Skills

Strong experience as a Data Engineer with hands-on Snowflake experience.
Strong programming experience with Python.
Advanced SQL skills.
Strong experience with PySpark / Apache Spark.
Experience developing ETL/ELT pipelines and data transformation workflows.
Strong understanding of data warehousing and data modeling concepts.
Experience working with large-scale datasets and cloud data platforms.
Good understanding of data quality, validation, troubleshooting, and performance optimization.
Excellent analytical and problem-solving skills.

Preferred Skills

Experience with Snowflake performance tuning and optimization.
Experience with AWS, Azure, or GCP.
Knowledge of Airflow, dbt, Databricks, Kafka, or cloud-native data services.
Experience with CI/CD, Git, and Agile development practices.
Experience working with structured and semi-structured data, including JSON and Parquet.