Remote Bi & Data Analytics Engineer (Power Bi/Sql)

Loveyourdevteam; Ciudad de Mendoza; Remote; ARS 139,312,000 - 201,229,000; Full time

Job summary

1801 Haleon Argentina S.A. is seeking a data engineer to build performant data pipelines, manage relational databases, and craft cloud-portable data lake environments using open formats and embedded analytics.

You will implement Medallion architecture, standardize on Parquet, Iceberg and Delta Lake, and run distributed processing with Databricks or Spark, while using Python and DuckDB for fast in-process analytics.

Qualifications

• Advanced SQL with deep PostgreSQL expertise.
• Hands-on experience building analytical pipelines in Python.
• Experience with DuckDB for localized processing.
• Familiarity with Parquet, Apache Iceberg and Delta Lake.
• Track record designing data lakes using Medallion architecture.
• Familiarity with Databricks / PySpark for distributed processing.

Responsibilities

• Design and implement resilient data pipelines.
• Manage relational databases and data lake environments.
• Develop cloud-portable data lake environments with open formats.
• Leverage embedded analytics with DuckDB in ETL workflows.
• Scale ETL and analytics workloads on Databricks or Spark.

Skills

Python

SQL

ETL pipelines

DuckDB

Tools

PostgreSQL

SQLite

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.