Data Engineering Jobs in Peru
There are 196 open Data Engineering jobs in Peru on freehire right now. 36 of them were posted recently. The skills employers ask for most often are sql, python and etl.
Most requested skills
- sql 65%
- python 60%
- etl 45%
- cloud 45%
- azure 42%
- databricks 35%
- aws 31%
- analytics 31%
How the work is done
- Remote 22 · 11%
- Hybrid 7 · 4%
Visa sponsorship offered in 10% of the 10 postings that state a position on it.
Seniority
- Senior 57
- Lead 12
- Junior 3
- Middle 3
- Principal 3
- Staff 2
Who is hiring
- 1000+ employees 19
- 501-1000 employees 18
- 51-200 employees 3
- 11-50 employees 1
- 201-500 employees 1
Data Engineer - AI/ML Data Pipelines & Insights
Build and maintain AI/ML data pipelines to power insights and models for a company focused on solving complex problems.
Data Engineer - Azure Pipelines, SQL & Python
Builds and maintains Azure data pipelines using SQL and Python for agile projects in a collaborative environment.
Data Engineer Azure
Builds and maintains Azure-based data pipelines and ETL/ELT workflows using SQL and Python for analytics and reporting.
GCP Data Engineer
Designs and builds data pipelines on Google Cloud Platform, integrating GenAI capabilities into data workflows using SQL and data warehousing tools.
GCP Data Engineer
Designs and builds data pipelines on Google Cloud Platform, integrating GenAI capabilities into data workflows using SQL and modern data engineering practices.
Data Engineer – ETL/ELT Pipelines & Analytics
Builds and maintains ETL/ELT pipelines and analytics warehouses to deliver governed data insights via SQL, Python, and visualization tools.
GCP Data Engineer — GenAI-Driven Data & Warehousing
Designs and builds scalable GCP data pipelines and integrates GenAI-driven workflows into modern data platforms.
DATA ENGINEER SENIOR
Senior data engineer designs and implements ETL pipelines, writes PL/SQL and Python code, and builds reports in Power BI/Qlik for large-scale client projects.
Customer Data Engineer
Build and maintain data pipelines in AWS to feed a centralized data lake, then create dashboards in Power BI that surface business insights for Ripley Perú’s teams.
Senior BI Data Engineer: Data Pipelines & Analytics
Designs and maintains data architectures (Data Lake/Warehouse) and analytics models to support retail banking campaigns and sales strategies using SQL and Python.
Data Engineer
Builds and maintains scalable data pipelines, models, and quality checks using SQL, ETL tools, and AI to clean, transform, and validate business data in BigQuery.
AI-Driven Data Engineer – Pipelines, Quality & Governance
Builds and maintains ETL pipelines with Airflow/Composer or Matillion, optimizes SQL queries, and applies AI to data transformation while ensuring quality and governance in a DataOps environment.
Data Engineer - Azure
Senior Data Engineer builds and maintains Azure-based data pipelines using Data Factory, Databricks, and PySpark to enable analytics and reporting for clients.
Data Engineer
Build and maintain data pipelines on AWS for a banking client, using Python, Spark, Glue, and Step Functions to process and model large datasets.
Data Engineer - Gcp
Build and optimize data pipelines on GCP, focusing on BigQuery performance, serverless data flows, and data governance while using Python, SQL, and DevOps practices.
Data Engineer (AWS + Databricks)
Builds and maintains AWS-based data pipelines and Databricks Lakehouse solutions for a banking client, using PySpark, Glue/EMR, and orchestration tools like Airflow.
Data Engineer Gcp
Designs and builds cloud data pipelines on GCP to deliver analytics and insights for energy-sector clients.
Data Engineer Senior
Senior Data Engineer designs and operates scalable data pipelines (CDC, ETL) on AWS/Databricks, builds medallion architecture with Delta Lake, and prepares gold tables for BI while ensuring data quality and compliance with Peruvian privacy laws.
Azure Data Engineer: Cloud Data Pipelines & Integration
Designs and builds Azure cloud data pipelines using Data Factory and Functions to integrate and transform data, migrating on-prem systems to the cloud while optimizing cost and performance.