As an AWS Data Architect, you will play a pivotal role in designing, implementing, and managing our data architecture on AWS, with a focus on using Snowflake and leveraging Python for data engineering tasks. You will be responsible for developing and optimizing data flows, managing API data ingestion, and crafting AWS Lambda functions to support our data ecosystem.
Your deep understanding of data warehousing principles and experience with DBT will be instrumental in ensuring data accuracy, efficiency, and scalability.
Key Responsibilities:
- Design and implement robust, scalable data architectures on AWS, integrating Snowflake as the primary data warehousing solution.
- Develop and optimize ETL/ELT processes using Python, ensuring efficient data transformation and loading.
- Architect and manage data pipelines from a variety of sources, including API data ingestion, with a focus on automation and scalability.
- Write and maintain AWS Lambda code for various data operations, ensuring high performance and reliability.
- Apply in-depth knowledge of data warehousing principles to ensure data integrity, quality, and security across the platform.
- Collaborate with cross-functional teams, including data analysts, engineers, and business stakeholders, to understand data needs and deliver comprehensive data solutions.
- Continuously evaluate and incorporate new technologies and methodologies to enhance data architecture and analytics capabilities.
- Provide expertise and guidance on best practices for data modeling, SQL queries, and performance optimization.
- Lead projects related to supply chain data analytics, leveraging previous experience in the domain to drive insights and improvements.
Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Information Technology, or related field.
- Minimum of 5 years of experience in data architecture, specifically with AWS cloud services.
- Proficient in Snowflake, Python for data engineering, and SQL.
- Strong experience in developing and managing ETL/ELT processes, data pipelines, and AWS Lambda functions.
- Deep understanding of data warehousing principles and best practices.
- Familiarity with data modeling, query optimization, and database management.
