Power Bi & Sql Analytics Engineer

Praxy; Ciudad de Mendoza; On-site; ARS 400,000 - 800,000; Full time

Job summary

Praxy is seeking a data engineering professional to build performant data processing pipelines and manage relational databases. The role involves constructing cloud-portable data lakes with open formats and embedding analytical engines for fast processing.

You will design resilient pipelines using Medallion architecture (Bronze/Silver/Gold) and leverage DuckDB with Python for localized analytics within ETL workflows. Strong PostgreSQL expertise is essential.

Qualifications

• Strong SQL with PostgreSQL expertise and knowledge of SQLite.
• Hands-on experience building analytical pipelines in Python with DuckDB.
• Experience with open table/storage formats: Parquet, Iceberg, Delta Lake.

Responsibilities

• Design reusable, resilient data processing pipelines in Python.
• Build and manage analytical pipelines using Medallion architecture.
• Standardize storage formats for cross-cloud compatibility.
• Architect and optimize PostgreSQL schemas for production use.

Skills

SQL proficiency

PostgreSQL expertise

Python

DuckDB

Tools

Databricks

PySpark

Parquet

Iceberg

Delta Lake

SQLite

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.