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