Data Engineer - Databricks Lakehouse & PySpark

AgileEngine; Capital; Presencial; COP 189.167.000 - 315.278.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Growth without limits

Competitive compensation

Flexibility: 100% remote

Meaningful, modern projects

Collaborative culture

Well-being & support

Descripción de la vacante

AgileEngine is looking for a Middle Data Engineer to modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will design and operate batch and streaming pipelines using PySpark, Delta Lake, and Databricks Workflows.

You will model a medallion architecture, migrate legacy workloads with minimal disruption, and collaborate with DevOps and analytics teams to ensure data quality, governance, and security while delivering scalable production data platforms.

Formación

• 3+ years of professional experience in data engineering with Spark and cloud data architectures.
• Hands-on experience building data pipelines with Databricks, Spark PySpark, and Delta Lake.
• Advanced SQL and Python with strong data modeling across dimensional and Lakehouse patterns.
• Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Workflow orchestration experience with Databricks Workflows, Airflow, or Azure Data Factory.
• Experience with legacy platform migrations, ETL modernization, or data hygiene porting.
• Strong problem-solving, collaboration, and communication skills.
• Familiarity with Unity Catalog, data governance, access control, and PII handling.
• Experience with dbt or equivalent transformation framework.
• Familiarity with secure coding standards and industry security best practices.
• Experience delivering production data platforms at scale.
• Upper-intermediate English level.

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.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

Conocimientos

Databricks

Spark PySpark

Advanced SQL

Python

Structured Streaming

Kafka

Airflow

Azure Data Factory

Unity Catalog

dbt

Herramientas

Delta Lake

Databricks

PostgreSQL

Descripción del empleo

AgileEngine is looking for a Middle Data Engineer to modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will design and operate batch and streaming pipelines using PySpark, Delta Lake, and Databricks Workflows.

You will model a medallion architecture, migrate legacy workloads with minimal disruption, and collaborate with DevOps and analytics teams to ensure data quality, governance, and security while delivering scalable production data platforms.