Data Engineer – Support & Delivery | Azure Databricks, ADF & SQL (36134)

Data Engineer – Support & Delivery | Azure Databricks, ADF & SQL

Latin America (LATAM) – 100% Remote

Full-Time Contract (4 months to start)

Monday to Friday | 8 hours per day (40 hours per week) (Eastern Time (EST))

English (Mandatory)

Position Overview

We are seeking an Intermediate Data Engineer – Support & Delivery to join an enterprise cloud data operations team, supporting and developing data solutions within a Microsoft Azure environment.

This hands-on position combines production support, troubleshooting, operational maintenance, and data engineering delivery, with a primary focus on supporting existing data pipelines and systems. Depending on business needs, new development and project delivery may account for up to 50% of the role.

Strong hands-on Azure Databricks experience is essential. The successful candidate must be comfortable independently troubleshooting production issues, maintaining existing data workflows, and developing or enhancing data pipelines using Databricks, Azure Data Factory (ADF), Python, and SQL.

This opportunity is suited to an intermediate-level Data Engineer who brings practical experience across both production support and data solution development, rather than someone focused exclusively on monitoring or basic technical support.

Key Responsibilities

Production Support & Data Operations

• Monitor, maintain, and troubleshoot production data pipelines and ETL/ELT workflows using Azure Databricks, Azure Data Factory (ADF), and SQL.
• Investigate and resolve pipeline failures, data processing errors, performance issues, and data availability problems.
• Provide hands-on production support, including incident investigation, ticket resolution, root cause analysis, and escalation.
• Execute SQL queries to validate data, investigate discrepancies, and troubleshoot processing issues.
• Monitor data ingestion, transformation, and orchestration processes to ensure operational stability.
• Identify recurring issues and implement sustainable technical solutions.
• Maintain technical documentation, support records, and operational procedures.
• Collaborate with technical teams, business users, and stakeholders to ensure reliable data operations.
  
  

Data Engineering & Solution Delivery

• Design, develop, enhance, and maintain scalable data pipelines using Azure Databricks, PySpark, SQL, and Azure Data Factory.
• Build and optimize data ingestion, transformation, and orchestration workflows.
• Develop and maintain data models, schemas, and reporting-ready datasets.
• Work with Delta Lake, Spark SQL, and Databricks notebooks for data processing and transformation.
• Optimize pipeline performance through query tuning, partitioning, and other performance improvements.
• Participate in the development, testing, deployment, and enhancement of new data engineering solutions.
• Apply best practices for data quality, validation, governance, security, and deployment.
• Support ongoing project deliverables while balancing production support priorities.
  
  

Required Skills & Qualifications

• Intermediate-level Data Engineering experience, including hands-on development and production support of cloud-based data pipelines.
• Strong hands-on Azure Databricks experience (Mandatory), including troubleshooting, development, and maintenance of production data solutions.
• Practical experience with PySpark, Spark SQL, Delta Lake, and Databricks notebooks.
• Strong Azure Data Factory (ADF) experience, including pipeline development, orchestration, monitoring, and troubleshooting.
• Strong SQL skills, including querying, data validation, troubleshooting, and performance optimization.
• Proficiency in Python for data engineering, automation, and data transformation.
• Experience developing, maintaining, and supporting ETL/ELT pipelines in cloud environments.
• Demonstrated production support experience, including incident management, ticket resolution, troubleshooting, and root cause analysis.
• Understanding of data modelling, data warehousing, lakehouse architecture, and data integration concepts.
• Experience contributing to both ongoing operational support and new data engineering delivery.
• Ability to work independently, manage competing priorities, and communicate effectively with technical and business stakeholders.
  
  

Preferred Qualifications

• Experience with Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Functions, and related Azure data services.
• Familiarity with Databricks Unity Catalog, data governance, and access management.
• Experience with Microsoft SQL Server.
• Experience with CI/CD, Azure DevOps, GitHub Actions, or Terraform.
• Exposure to Snowflake, dbt, Airflow, or similar data engineering and orchestration technologies.
• Familiarity with Kafka, Azure Event Hubs, or other streaming technologies.
• Experience supporting enterprise-scale data environments.
• Familiarity with Remote Desktop (RDP) for accessing and troubleshooting source systems.
• Databricks certifications are an asset.
  
  

Ideal Candidate

The ideal candidate is a hands-on Intermediate Data Engineer with strong Azure Databricks expertise who is equally comfortable resolving production issues and delivering new data engineering solutions.

You can independently troubleshoot an ADF pipeline failure, investigate SQL or Databricks processing issues, resolve production support tickets, and develop or optimize data pipelines as project requirements arise.

You are technically capable, proactive, comfortable working in a remote team environment, and able to balance operational support responsibilities with ongoing development and delivery.

This is not a basic monitoring or help desk position. Strong Databricks proficiency, production troubleshooting experience, and hands-on data engineering delivery capabilities are essential.

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