Databricks Data Engineer: Lakehouse Pipelines

Agileengine; Victoria; Híbrido; ARS 150.912.000 - 211.276.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive USD-based compensation

A selection of exciting projects

Flextime

Descripción de la vacante

AgileEngine is seeking a Data Engineer to design and build batch and streaming data pipelines on Databricks using PySpark and Delta Lake. You will migrate legacy ETL workloads to a governed Lakehouse, model a medallion architecture, and enable analytics and AI use cases with strong SQL and Python skills.

You will also optimize Spark jobs, implement data governance with Unity Catalog, and collaborate across DevOps, platform, and analytics teams in an Agile environment.

Formación

• :+4 years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.
• Strong hands-on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.
• Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.
• Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.
• Strong problem-solving, collaboration, and communication skills, including mentoring junior engineers and explaining data concepts to non-technical stakeholders.
• Familiarity with Unity Catalog, data governance, access control, and PII handling.
• Experience with dbt or an equivalent transformation framework.
• Familiarity with secure coding standards and industry security best practices.

Responsabilidades

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion (bronze/silver/gold) architecture serving analytics, reporting, and machine learning consumers.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with validated data parity and minimal business disruption.
• Use Claude or GitHub Copilot as a development accelerator, generating code scaffolding, writing and reviewing tests, creating documentation, and prototyping solutions.
• Write clean, well-tested Python and SQL; maintain high standards through code review and documentation.
• Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing.
• Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks.
• Debug, troubleshoot, and resolve pipeline failures, data defects, and production incidents.
• Participate in Agile or product-centric delivery practices, including sprint planning and retrospectives.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

Conocimientos

Databricks

Apache Spark

Delta Lake

SQL

Python

Structured Streaming

Kafka

Airflow

Unity Catalog

dbt

Herramientas

Databricks

Airflow

Azure Data Factory

PostgreSQL

Kafka

Unity Catalog

dbt

Descripción del empleo

AgileEngine is seeking a Data Engineer to design and build batch and streaming data pipelines on Databricks using PySpark and Delta Lake. You will migrate legacy ETL workloads to a governed Lakehouse, model a medallion architecture, and enable analytics and AI use cases with strong SQL and Python skills.

You will also optimize Spark jobs, implement data governance with Unity Catalog, and collaborate across DevOps, platform, and analytics teams in an Agile environment.