Data Engineer

Data Engineer

Remote position in México

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.

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 Data Lake Storage (ADLS)

• Azure Synapse Analytics

• Azure Event Hubs

• Azure Functions

• Azure API Management

• Azure DevOps

• Experience with Databricks Lakehouse architecture, Delta Lake, and Delta Tables.

• Expertise in API development, API integration, RESTful services, and microservices architecture.

• Experience processing high-volume and high-frequency data with low-latency requirements.

• Strong knowledge of real-time data ingestion and streaming technologies such as Kafka, Azure Event Hubs, or Kinesis.

• Experience with Spark, Hadoop, and distributed data processing frameworks.

• Hands-on experience with OpenShift, Kubernetes, Docker, and containerized deployments.

• Experience with workflow orchestration tools such as Apache Airflow and Azure Data Factory.

• Understanding of data governance, data security, and compliance best practices.

• Preferred Qualifications

• Experience with Delta Live Tables (DLT), Auto Loader, and Change Data Capture (CDC).

• Knowledge of DataOps, CI/CD, and Infrastructure as Code (IaC).

• Familiarity with event-driven architectures and real-time analytics platforms.

• Azure Data Engineer (DP-203) and Databricks certifications