Role Overview

The Data Engineer is responsible for designing, building, and maintaining scalable, cloud native data pipelines and data infrastructure that support analytics, reporting, business

intelligence, and real-time data processing. This role ensures that data is accessible,

reliable, secure, and optimized for performance across enterprise platforms. The Data

Engineer collaborates with business stakeholders, analysts, data scientists, and

application teams to deliver high-quality data solutions using modern cloud, big data, and

API-driven technologies.

Key Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines for batch and real-time

data processing.

Build and optimize data models, Delta Tables, and Lakehouse architectures to

support analytics and reporting.

Develop and integrate RESTful APIs and data services to facilitate seamless data

exchange across enterprise systems.

Implement real-time and high-frequency data ingestion frameworks using streaming

technologies and event-driven architectures.





Design and manage cloud-native data solutions leveraging Azure services including

Azure Data Factory, Azure Databricks, ADLS, Event Hubs, and Synapse Analytics.

Develop and optimize Databricks Spark applications for large-scale data

transformation and processing.

Ensure data quality, governance, security, and compliance across data platforms.
Collaborate with data scientists, analysts, application teams, and business

stakeholders to deliver scalable data solutions.

Troubleshoot, monitor, and optimize pipeline performance and data platform

reliability.

Support DataOps and CI/CD practices for data pipeline deployment and

automation.

Required Skills & Qualifications

Strong proficiency in SQL and relational databases such as Oracle, SQL Server, and

MySQL.

Strong programming skills in Python, PySpark, PL/SQL, Java, or Scala.
Hands-on experience with Azure Cloud technologies:
Azure Data Factory (ADF)
Azure Databricks
Azure Dat

📌 Data Engineer (Guadalajara)
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