Remote Data Analytics Engineer: Spark Pipelines & Lakes

Toptal; Ciudad de Mendoza; Remote; ARS 600,000 - 900,000; Full time

Job summary

Toptal in Argentina seeks a data engineer to build robust data pipelines and manage open storage formats for cross-cloud analytics. You will implement Medallion architecture (Bronze/Silver/Gold) and optimize data lakes with Parquet, Iceberg and Delta Lake.

Expertise in Python, PostgreSQL, and DuckDB is required to design scalable ETL workflows and embedded analytics. Familiarity with Databricks / PySpark is a plus.

Qualifications

• Advanced SQL proficiency with deep PostgreSQL expertise and SQLite knowledge.
• Hands-on experience building 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.

Responsibilities

• Pipeline Design: Construct resilient data processing pipelines in Python using Medallion architecture to transform raw data into analytics-ready assets.
• Open Storage Management: Standardize data storage using open formats including Parquet, Apache Iceberg, and Delta Lake for 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.

Skills

SQL

PostgreSQL

SQLite

Python

DuckDB

Parquet

Apache Iceberg

Delta Lake

Medallion architecture

Tools

Databricks

PySpark

Spark

DuckDB

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.