Job description

Take your career to the next level with Amaris Consulting as a AWS DATA ENGINEER-REMOTE SPAIN. Be part of an international team, thrive in a global group with a €800M turnover and over 1,000 clients worldwide, and an agile environment. The ideal candidate will have a strong background in front-end technologies and a passion for delivering exceptional user experiences.

✍️WHAT WOULD YOU NEED? ✍️

Experience:

Minimum of [3] years of experience in (SNOWFLAKE) data engineering. Proven experience with AWS services, including Glue, Lambda, and S3. Hands-on experience with Snowflake, including data modeling and performance tuning. Strong proficiency in Python programming and SQL query writing. In-depth knowledge of AWS architecture and best practices. Experience with ETL/ELT processes and data pipeline orchestration. Familiarity with data warehousing concepts and relational databases. Excellent problem-solving skills and attention to detail.

Certifications:

AWS Certified Data Analytics – Specialty or AWS Certified Solutions Architect – Associate is a plus. Snowflake certifications are highly desirable.

Desired:

Data Pipeline Development:

Design, develop, and maintain scalable data pipelines using AWS Glue and other AWS services. Implement ETL/ELT processes to transform and move data across systems.

Data Integration and Storage:

Work with Snowflake to manage data storage, optimize performance, and ensure data security. Integrate diverse data sources, including structured and unstructured data, into Snowflake.

Serverless Computing:

Utilize AWS Lambda for serverless data processing and real-time data analytics. Automate data workflows and manage event-driven architectures using AWS services.

Programming and Scripting:

Develop, test, and maintain Python scripts for data processing tasks. Write complex SQL queries to support data extraction, transformation, and loading (ETL).

Data Quality and Performance Optimization:

Ensure data quality, integrity, and accuracy across all data pipelines. Monitor and optimize the performance of data systems, ensuring minimal latency and high availability.

Collaboration and Documentation:

Work closely with data analysts, data scientists, and other stakeholders to understand data needs and provide solutions. Document data processes, pipelines, and systems architecture for ongoing maintenance and future reference.