Data Engineer (Core & Analytics)

Wakapi; Ciudad de Mendoza; Presencial; ARS 137.091.000 - 228.484.000; Jornada completa

Descripción de la vacante

Wakapi is seeking a data engineer to build scalable data pipelines and manage cloud-ready data lakes. You will implement Medallion architecture and run analytics-ready transformations in Python with DuckDB.

You'll optimize PostgreSQL schemas, coordinate across open storage formats such as Parquet, Iceberg, and Delta Lake, and work with Databricks/Spark to scale workloads in a multi-cloud environment.

Formación

• Advanced SQL proficiency with PostgreSQL expertise.
• 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.
• Knowledge of SQL, PostgreSQL, SQLite, Medallion Architecture, DuckDB, Parquet/Iceberg/Delta, Databricks/Spark.

Responsabilidades

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

Conocimientos

SQL

PostgreSQL

Python

DuckDB

Databricks

PySpark

Medallion Architecture

Open Formats

Herramientas

Databricks

Apache Spark

Parquet

Iceberg

Delta Lake

PostgreSQL

SQLite

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 -

Wakapi Web