Data Analytics Engineer | .Net / Sql | Remote

Toptal; Ciudad de Mendoza; Remote; ARS 900,000 - 1,300,000; Full time

Job summary

Toptal is seeking a data engineering professional to design resilient ETL pipelines and standardized data lakes. You will implement Medallion architecture, leverage Parquet/Iceberg/Delta formats, and perform embedded analytics with Python and DuckDB.

You will optimize PostgreSQL schemas and support lightweight SQLite storage, while enabling distributed processing with Databricks or Spark in a cross-cloud setup.

Qualifications

• Advanced SQL with PostgreSQL expertise and SQLite knowledge.
• Hands-on experience building analytical pipelines with Python and DuckDB.
• Mastery of open formats like Parquet, Iceberg, and Delta Lake.
• Proven track record designing data lakes using Medallion architecture.
• Familiarity with Databricks and PySpark for distributed processing.

Responsibilities

• Pipeline design: build robust data processing pipelines in Python using Medallion architecture.
• Open storage management: standardize data in Parquet, Iceberg, Delta Lake for cross-cloud use.
• Embedded analytics: use DuckDB with Python for fast, localized ETL analytics.
• Database operations: architect and optimize PostgreSQL schemas and lightweight SQLite storage.
• Distributed compute: scale ETL and analytics with Databricks or Spark.

Skills

Advanced SQL (PostgreSQL)

Python + DuckDB

Open table/storage formats

Medallion architecture

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.