Remote Data Analytics Engineer - Ga4 & Gtm Tracking Lead

Fullstack; Ciudad de Mendoza; Remote; ARS 1,800,000 - 3,000,000; Full time

Job summary

Fullstack in Mendoza, Argentina is seeking a data engineer to build and optimize analytical pipelines, manage relational databases, and create cloud-portable data lake environments using open storage formats.

You will implement Medallion architecture (Bronze/Silver/Gold), leverage DuckDB with Python, and work with PostgreSQL and SQLite for storage, scaling workloads with Databricks or Spark.

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

• Design resilient data pipelines in Python using Medallion architecture.
• Standardize data storage with Parquet, Iceberg, Delta Lake across clouds.
• Leverage DuckDB for fast localized analytics within ETL workflows.
• Architect and optimize PostgreSQL schemas; use SQLite for embedded storage.
• Scale ETL workloads with Databricks or Apache Spark.

Skills

Python

SQL

Tools

PostgreSQL

SQLite

DuckDB

Parquet

Apache Iceberg

Delta Lake

Databricks

PySpark

Job description

The Role:

Builds performant data processing pipelines, manages relational databases, and constructs cloud-portable data lake environments using modern open storage formats and embedded analytical engines.

Responsibilities:

Pipeline Design: Construct resilient data processing pipelines in Python using Medallion architecture principles (Bronze/Silver/Gold) to transform raw data into analytics-ready assets.

Open Storage Management: Standardize data storage using open formats including Parquet, Apache Iceberg, and Delta Lake to ensure cross-cloud compatibility.

Embedded Analytics: Leverage DuckDB integrated with Python for fast, lightweight, and performant localized data processing within ETL workflows.

Database Operations: Architect and optimize production PostgreSQL schemas while utilizing SQLite for embedded or lightweight localized storage needs.

Distributed Compute: Scale heavy ETL, data transformation, and analytics workloads using Databricks or Apache Spark

Requirements:

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.