Data Platform & Analytics Engineer (SaaS)

Payo Argentina; Ciudad de Mendoza; On-site; ARS 1,800,000 - 3,000,000; Full time

Job summary

Payo Argentina is seeking a data engineer to design and optimize end-to-end data pipelines in Python, leveraging Medallion architecture to transform raw data into analytics-ready assets. The role focuses on robust ETL, standardized storage with Parquet, Iceberg, and Delta Lake, and embedded analytics with DuckDB.

Responsibilities include building scalable pipelines, managing PostgreSQL schemas, and enabling distributed processing with Databricks/PySpark across multi-cloud environments.

Qualifications

• Advanced SQL proficiency with deep PostgreSQL expertise and working knowledge of SQLite.
• Hands-on experience building robust analytical pipelines using Python and DuckDB.
• Mastery of open table and storage formats (Parquet, Apache Iceberg, Delta Lake).
• Proven track record designing data lakes using the Medallion data processing architecture.
• Familiarity with the Databricks / PySpark ecosystem for distributed processing.

Responsibilities

• Pipeline Design: Construct resilient data processing pipelines in Python using Medallion architecture principles (Bronze/Silver/Gold).
• Open Storage Management: Standardize data storage using open formats for cross-cloud compatibility.
• Embedded Analytics: Leverage DuckDB integrated with Python for fast localized data processing within ETL workflows.
• Database Operations: Architect and optimize production PostgreSQL schemas and SQLite for embedded storage needs.
• Distributed Compute: Scale heavy ETL and analytics workloads using Databricks or Apache Spark.

Skills

Advanced SQL

PostgreSQL expertise

Python

DuckDB

Tools

Databricks

PySpark

Apache Spark

Delta Lake

Parquet

Iceberg

Job description

Payo Argentina is seeking a data engineer to design and optimize end-to-end data pipelines in Python, leveraging Medallion architecture to transform raw data into analytics-ready assets. The role focuses on robust ETL, standardized storage with Parquet, Iceberg, and Delta Lake, and embedded analytics with DuckDB.

Responsibilities include building scalable pipelines, managing PostgreSQL schemas, and enabling distributed processing with Databricks/PySpark across multi-cloud environments.