Data Engineer - Snowflake

Key Responsibilities: Design & Build: Develop scalable, resilient ETL workflows and real-time data pipelines using Python, PySpark, and AWS services. Cloud Engineering: Utilize AWS services such as Glue, Lambda, EC2, RDS, and S3 to build efficient cloud-based data architectures. Snowflake Integration: Design and maintain data models, pipelines, and ingestion processes within Snowflake , ensuring performance and scalability. API Development: Integrate RESTful and other APIs to ingest and synchronize external and internal datasets. Optimization: Monitor and tune the performance of data workflows, pipelines, and queries for minimal latency and high throughput. Collaboration: Partner with data analysts, scientists, and stakeholders to define and deliver data requirements. Required Skills & Qualifications: Experience Level: 4+ years in data engineering or related roles. Programming Expertise: Proficient in Python and PySpark , with a strong background in data transformation and API integration. AWS Mastery: Deep hands-on experience with AWS Glue, Lambda, EC2, RDS, and S3. ETL & Real-Time Pipelines: Proven ability to build and scale batch and streaming data workflows. Snowflake: Hands-on experience with Snowflake data warehouse , including data modeling, Snowpipe, and performance tuning. API Integration: Strong understanding of REST APIs, JSON, and secure authentication protocols. Data Systems: Proficiency in relational databases (e.g., PostgreSQL, MySQL) and best practices in data warehousing. Problem Solver: Strong analytical skills with a knack for identifying and solving complex data challenges.