Data Engineer & Support Specialist – Azure Data Factory, SQL & Databricks

  • Location: No Location Set
  • Type: Contract
  • Job #36058

Data Engineer & Support Specialist – Azure Data Factory, SQL & Databricks

Location: Remote – Latin America (LATAM)
Employment Type: Part-Time / Freelance Contract
Schedule: Monday to Friday | 20 hours/week – see below for more information
Compensation: USD – Hourly Rate
Experience Level: Junior to Intermediate
Language: Spanish Required

​​​​​​​Working Hours & Availability
Location: Remote – LATAM Engagement: Part-Time / Freelance Contract Schedule: 4 hours daily, aligned with Chilean business hours.
Candidates must be available to begin work at 6:00 AM Chile local time, supporting the Chile-based team.
For reference:

  • Until October 31, 2026: 6:00 AM Chile = 5:00 AM EDT
  • Beginning November 1, 2026: 6:00 AM Chile = 4:00 AM EST

Important: Candidates must be comfortable with this early-morning schedule and able to consistently support the required working hours.
 

Position Overview

We are seeking a Junior to Intermediate Data Engineer & Support Specialist for a remote, part-time contract supporting data operations within an enterprise mining environment in Chile.

This hands-on role focuses on Azure Data Factory (ADF), Microsoft SQL Server, and Azure Databricks, including basic data pipeline monitoring, SQL queries, data availability verification, and technical troubleshooting.

The ideal candidate is a practical, self-sufficient technical professional comfortable supporting existing data environments, investigating issues, and escalating more complex problems when necessary.

Key Responsibilities

  • Monitor and troubleshoot Azure Data Factory (ADF) pipelines and data integration workflows.

  • Write and execute basic SQL queries using Microsoft SQL Server to retrieve, validate, and investigate data.

  • Perform basic support activities within Azure Databricks, including data availability checks.

  • Access source systems through Remote Desktop (RDP) when required to verify data availability.

  • Investigate data access, pipeline execution, and data processing issues.

  • Identify and escalate technical problems requiring senior engineering expertise.

  • Communicate findings, provide status updates, and document troubleshooting activities.

  • Support knowledge transfer and a smooth transition of responsibilities to a Senior Data Engineer.

Required Qualifications

  • Fluent or professionally proficient in Spanish – Mandatory.

  • Hands-on experience with Azure Data Factory (ADF), including pipeline monitoring and basic troubleshooting.

  • Working knowledge of Microsoft SQL Server and SQL queries, including basic data retrieval and validation.

  • Basic experience with Azure Databricks and familiarity with data processing environments.

  • Comfortable using Remote Desktop (RDP) to access and investigate source systems.

  • Ability to independently perform routine data support and troubleshooting activities.

  • Strong communication, documentation, and problem-solving skills.

  • Must reside in Latin America (LATAM).

  • Must be available Monday to Friday, 6:00 AM – 10:00 AM Eastern Time.

Preferred Qualifications

  • Experience supporting Azure cloud-based data platforms.

  • Familiarity with ETL/ELT processes, data integration, and data validation.

  • Previous experience providing remote data engineering or production support.

  • Exposure to mining, industrial, or enterprise data environments.

Contract Details

  • Remote: LATAM-based candidates only.

  • Engagement: Part-time freelance contract.

  • Hours: 4 hours per day / 20 hours per week.

  • Payment: USD hourly rate.

  • Duration: Interim assignment, with responsibilities transitioning to a Senior Data Engineer once system access is established.

Ideal Candidate Profile

A Junior to Intermediate Data Engineer, Data Support Analyst, or ETL Support Specialist with practical exposure to Azure Data Factory, SQL Server, and Databricks.

This position emphasizes hands-on technical support, data availability, pipeline monitoring, and basic troubleshooting rather than advanced data architecture or complex engineering development.

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