Data Engineer (Core & Analytics)

Wakapi; Departamento Capital; Presencial; ARS 106.226.000 - 166.927.000; Jornada completa

Descripción de la vacante

Wakapi is seeking a data engineer to build performant data processing pipelines, manage relational databases, and create cloud-portable data lake environments using open storage formats and embedded analytical engines. You will shape end-to-end data workflows from ingestion to analytics-ready assets.

Responsibilities include designing resilient pipelines in Python, standardizing storage with Parquet/Iceberg/Delta Lake, and leveraging DuckDB for fast local analytics within ETL processes, while

Formación

• Advanced SQL skills with strong PostgreSQL expertise.
• Hands-on experience building analytical pipelines in Python.
• Experience with open storage formats and data lake design.

Responsabilidades

• Design robust data pipelines that transform raw data into analytics-ready assets.
• Manage relational databases and optimize PostgreSQL schemas.
• Standardize storage using Parquet, Iceberg, and Delta Lake for cross-cloud compatibility.
• Build and optimize cloud-portable data lake environments.
• Leverage DuckDB within ETL workflows for fast local analytics.

Conocimientos

Advanced SQL

Python

DuckDB

Medallion Architecture

Databricks/Spark

PostgreSQL

Herramientas

PostgreSQL

SQLite

Python

DuckDB

Parquet

Iceberg

Delta Lake

Databricks

Spark

Medallion Architecture

Descripción del empleo

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.

Python & SQL | PostgreSQL & SQLite | Medallion Architecture | DuckDB | Parquet / Iceberg / Delta | Databricks / Spark

C - PGS - 29092026