Senior Data Engineer - Mexico

Papigen; México; On-site; MXN 900,000 - 1,500,000; Full time

Job summary

Papigen is seeking a Senior Data Engineer / Databricks Engineer to design, develop, and optimize enterprise data pipelines using Azure Databricks, ADLS, ADF, PostgreSQL, and Azure DevOps in Mexico City. The role focuses on building ingestion, transformation, and reporting-ready datasets, implementing Medallion architecture, data quality controls, and CI/CD processes in a production environment.

You will collaborate with architects, BI teams, QA, and business analysts to ensure robust pipelines

Qualifications

• 8+ years of IT experience with data engineering focus.
• Azure Databricks notebooks, transformations, and pipeline development.
• ADLS storage and data lake architectures.
• PySpark, Python, and SQL proficiency for data pipelines.
• ETL/ELT development and data integration with strong data quality controls.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and integration.
• Develop Databricks notebooks and reusable data engineering components using PySpark and SQL.
• Implement Medallion Architecture (Bronze, Silver, Gold).
• Integrate data sources across operational, financial, accounting, portfolio, risk, and enterprise domains.
• Create curated analytical datasets and data models for reporting and analytics.
• Implement data-quality checks, validation rules, and reconciliation controls.
• Tune performance of Databricks workloads and PostgreSQL data processing.
• Monitor scheduled data loads and troubleshoot pipeline issues in production.
• Support data lineage, metadata, and source-to-target mapping; maintain runbooks.

Skills

ETL/ELT development

Data modeling

SQL proficiency

Data quality and reconciliation

Production troubleshooting

Tools

Azure Databricks

Azure Data Lake Storage

PySpark

Python

PostgreSQL

Azure Data Factory

Azure DevOps

Git

Job description

Role Overview

We are looking for a Senior Data Engineer / Databricks Engineer with 8+ years of experience to design, develop, optimize, and support enterprise data pipelines and analytical datasets.

The candidate will work with Azure Databricks, Azure Data Lake, Azure Data Factory, PostgreSQL, and Azure DevOps to deliver reliable data ingestion, transformation, reconciliation, and reporting-ready data solutions.

Key Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and integration.
• Develop Databricks notebooks and reusable data engineering components using PySpark and SQL.
• Implement Medallion Architecture (Bronze, Silver, and Gold layers).
• Integrate operational, financial, accounting, portfolio, risk, and enterprise data sources.
• Develop curated analytical datasets and data models for reporting and analytics.
• Implement data-quality checks, validation rules, reconciliation controls, and exception handling.
• Optimize Databricks workloads, SQL queries, and PostgreSQL-related data processing.
• Monitor scheduled data loads and troubleshoot pipeline failures and production incidents.
• Support data lineage, metadata, source-to-target mapping, and technical documentation.
• Implement CI/CD, source control, deployment validation, and release-management processes.
• Collaborate with architects, BI developers, QA analysts, business analysts, and DevOps teams.
• Maintain pipeline documentation, operational runbooks, and monitoring reports.

Mandatory Skills & Experience

• 8+ years of overall IT experience, with strong relevant Data Engineering experience.
• Azure Databricks - notebooks, transformations, and pipeline development.
• Azure Data Lake Storage (ADLS).
• PySpark, Python, and SQL.
• ETL/ELT pipeline development and data integration.
• Medallion Architecture - Bronze, Silver, and Gold layers.
• Strong SQL and PostgreSQL skills.
• Data quality, reconciliation, validation, and exception handling.
• Performance tuning and production troubleshooting.
• Data pipeline monitoring and scheduled job support.

Preferred Skills

• Azure Data Factory (ADF).
• Azure DevOps, Git, CI/CD pipelines, and automated deployments.
• Data lineage, metadata management, and source-to-target mapping.
• Delta Lake and lakehouse data modeling.
• Batch processing, job recovery, and operational automation.
• Financial services, accounting, portfolio, risk, or reinsurance data.
• Release management, root-cause analysis, and production support.