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

Location: Argentina – Remote

Job Type: Full Time

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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

📌 Data Engineer (AWS, SQL, Python) (Argentina)
🏢 E-Solutions
📍 Argentina