Remote Databricks Data Engineer: Lakehouse & AI Pipelines

AgileEngine; Región Centro; Presencial; MXN 600.000 - 900.000; 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 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 with PySpark and Delta Lake, applying a medallion architecture across bronze, silver, and gold layers.

You will collaborate with DevOps and analytics engineers, optimize Spark jobs for performance and cost, implement governance with Unity Catalog, and leverage AI tools like Claude and GitHub Copilot to accelerate

Formación

• 3+ 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.
• 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.

Conocimientos

Databricks

Spark PySpark

SQL

Python

Data modeling

Unity Catalog

dbt

Structured Streaming

Airflow

Azure Data Factory

Security best practices

PII handling

English (Upper‑intermediate)

Herramientas

Databricks

Terraform

PostgreSQL

Dynatrace

CloudWatch

Databricks Workflows

dbt

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 with PySpark and Delta Lake, applying a medallion architecture across bronze, silver, and gold layers.

You will collaborate with DevOps and analytics engineers, optimize Spark jobs for performance and cost, implement governance with Unity Catalog, and leverage AI tools like Claude and GitHub Copilot to accelerate