Senior ML & Data Analytics Engineer - Data Lake Architect

Felix; Ciudad de Mendoza; Remote; ARS 2,400,000 - 4,200,000; Full time

Job summary

1801 Haleon Argentina S.A. is seeking a data engineering professional to build high-performance data processing pipelines and manage cloud-portable data lake environments.

You will standardize storage with Parquet, Iceberg, and Delta Lake, and leverage DuckDB for embedded analytics within ETL workflows. The role requires advanced SQL with PostgreSQL expertise, Python-based pipeline development, and experience with distributed processing using Databricks or PySpark.

Qualifications

• Advanced SQL proficiency with PostgreSQL expertise and SQLite knowledge.
• Hands-on experience building analytical pipelines using Python and DuckDB.
• Mastery of open table and storage formats (Parquet, 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

• Pipeline Design: Construct resilient data processing pipelines in Python using Medallion architecture principles (Bronze/Silver/Gold).
• Open Storage Management: Standardize data storage using Parquet, Iceberg, and Delta Lake for cross-cloud compatibility.
• Embedded Analytics: Utilize DuckDB within Python for fast localized data processing in ETL workflows.
• Database Operations: Design and optimize PostgreSQL schemas; use SQLite for embedded storage needs.
• Distributed Compute: Scale ETL and analytics workloads with Databricks or Apache Spark.

Skills

Advanced SQL

Python

DuckDB

Databricks

Tools

PostgreSQL

SQLite

Parquet

Iceberg

Delta Lake

PySpark

Job description

1801 Haleon Argentina S.A. is seeking a data engineering professional to build high-performance data processing pipelines and manage cloud-portable data lake environments.

You will standardize storage with Parquet, Iceberg, and Delta Lake, and leverage DuckDB for embedded analytics within ETL workflows. The role requires advanced SQL with PostgreSQL expertise, Python-based pipeline development, and experience with distributed processing using Databricks or PySpark.