Remote Data Engineer — Databricks Lakehouse & AI Pipelines

INGEPSY; Bogotá; Presencial; COP 120.000.000 - 180.000.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Growth opportunities

Competitive compensation

Remote work

Modern projects

Collaborative culture

Well-being programs

Descripción de la vacante

AgileEngine is seeking 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 and Delta Lake, applying a medallion architecture across bronze, silver, and gold layers.

You will migrate legacy workloads, validate data parity, and collaborate with DevOps and analytics teams to ensure observability, security, and compliance.

Formación

• 3+ years of professional data engineering experience with Spark and cloud data architectures.
• Hands-on with Databricks, Spark (PySpark), and Delta Lake.
• Advanced SQL and Python with 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.
• 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.
• 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.

Descripción del empleo

AgileEngine is seeking 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 and Delta Lake, applying a medallion architecture across bronze, silver, and gold layers.

You will migrate legacy workloads, validate data parity, and collaborate with DevOps and analytics teams to ensure observability, security, and compliance.