Senior Healthcare Analytics Engineer | Sql, Power Bi, Azure

Atomic Hr; Ciudad de Mendoza; On-site; ARS 1,800,000 - 3,200,000; Full time

Job summary

1801 Haleon Argentina S.A. is seeking an experienced data engineering professional to build resilient data pipelines and manage cross-cloud data lake environments using open storage formats.

You will deploy Python-based ETL workflows, integrate DuckDB for localized analytics, and design scalable PostgreSQL schemas while leveraging SQLite for embedded storage. The role emphasizes Medallion architecture, open formats (Parquet, Iceberg, Delta Lake), and distributed processing with Databricks or

Qualifications

• Advanced SQL proficiency with PostgreSQL expertise and SQLite knowledge.

Responsibilities

• Design data pipelines in Python using Medallion architecture to transform raw data into analytics-ready assets.
• Standardize storage with Parquet, Iceberg, Delta Lake for cross-cloud compatibility.
• Use DuckDB with Python for fast local analytics in ETL workflows.
• Architect and optimize PostgreSQL schemas; use SQLite for embedded storage.
• Scale ETL and analytics with Databricks or Apache Spark.

Skills

Advanced SQL

PostgreSQL

Python

DuckDB

Medallion architecture

Databricks PySpark

SQLite

Open formats Parquet

Iceberg

Delta Lake

Tools

DuckDB

Databricks

PySpark

PostgreSQL

SQLite

Parquet

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.