Data Engineer (AWS, SQL, Python)

Job Role: Data Engineer (AWS, SQL, Python)

Location: Argentina – Remote

Job Type: Full Time

Job Description:

Key Skills & Experience:

• Proficient in Python for developing reusable packages, scripting, automation, and working with REST APIs.
• Strong SQL and Snowflake expertise, including performance tuning and data modeling.
• Experience with Apache Airflow for orchestration and workflow monitoring.
• Hands-on with dbt for modular, version-controlled data transformations
• Solid experience with AWS services (e.g., S3, Lambda, IAM, CloudWatch) in data engineering workflows.
• Experience integrating and processing data from REST APIs.
• Understanding data quality, governance, and cloud-native troubleshooting.

Primary Skillset :(Must have)

• Great Communicator/Client Facing
• Individual Contributor and ability to work as a team.
• 100% Hands on in the mentioned skills

Programming Skills:

Python:

• Advanced Proficiency in Python concepts like Code Structures, Modules, Packages, Class, SubClass, Inheritance, Multi-Threading and Functional Programming.
• Experience in developing reusable Python packages for internal or public usage
• Proficiency in Pandas and NumPy for data analysis and manipulation
• Ability to write scripts for automating ETL processes and scheduling jobs using Airflow

SQL:

• Advanced SQL skills, including complex joins ,window functions, CTE's and subqueries
• Experience in optimizing SQL queries for performance and optimization in data warehouse technologies preferably Snowflake

DBT Core/ Cloud Proficiency:

• Experience in creating complex DBT models including full refresh, incremental models, snapshots and documentation. Ability to write and maintain DBT macros for reusable code
• Experience in creating custom DBT macros using jinja and Python allowing for reusable components within dbt models
• Knowledge on how to implement conditional logic in DBT through python

Testing and documentation:

• Proficiency in Python unit, integration and system test.
• Proficiency in implementing DBT tests for data validation and quality checks

Code Generation:

• Experience in generating code using configurations using python and jinja templates
• Version control:
• Experience in github, including implementing CI/CD process from scratch

AWS Expertise:

Data Storage solutions

In depth understanding of AWS S3 for data storage, including best practices for organization and security

Data Lakes and Data warehousing:

Understanding the architecture of data lakes vs data warehouses and when to use each

Experience with amazon Athena for querying data directly in s3 using SQL

Cloud Security:

Knowledge of AWS security best practices, including IAM roles, encryption, DBT profiles access configurations

Monitoring and Logging (nice to have):

Familiarity with AWS cloud watch for monitoring the pipelines and setting up alerts for workflow failures

Data Integration (nice to have):

Experience with AWS lambda for serverless data processing tasks

Workflow Orchestration (nice to have):

Proficiency in using Apache Airflow on AWS to design ,schedule and monitor complex data flows

Ability to integrate Airflow with AWS services and DBT models such as triggering a DBT model or EMR or reading from s3 writing to redshift