Remote Bi & Sql Analytics Engineer (Power Bi)

Virtustant; Ciudad de Mendoza; Remote; ARS 1,200,000 - 2,400,000; Full time

Job summary

Virtustant in Mendoza, Argentina seeks a data engineer to build performant data processing pipelines, manage relational databases, and construct cloud‑portable data lake environments using open formats and embedded analytics engines. You will design resilient pipelines in Python, standardize storage with Parquet, Iceberg, and Delta Lake, and leverage DuckDB for fast analytics within ETL workflows.

Strong PostgreSQL expertise is essential, with PySpark/Databricks familiarity for distributed

Qualifications

• Proficient in advanced SQL with PostgreSQL expertise.
• Experience building analytical pipelines with Python and DuckDB.
• Masters open table and storage formats (Parquet, Iceberg, Delta Lake).
• Proven track record designing data lakes using Medallion architecture.
• Familiar with Databricks / PySpark for distributed processing.

Responsibilities

• Design resilient data pipelines in Python per Medallion architecture.
• Standardize storage using Parquet, Iceberg, and Delta Lake.
• Leverage DuckDB for embedded analytics in ETL pipelines.
• Architect and optimize PostgreSQL schemas for production use.
• Scale ETL and analytics with Databricks or Spark.

Skills

Advanced SQL

Medallion architecture

Cross-cloud data lakes

Python

Tools

Python

DuckDB

PostgreSQL

SQLite

Databricks

PySpark

Parquet

Apache Iceberg

Delta Lake

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.