Azure Data Engineering Lead

Infosys; Monterrey; Hybrid; MXN 1,200,000 - 2,400,000; Full time

Job summary

Infosys is seeking a Lead Azure Data Engineer to drive architecture and design of scalable data platforms on Azure. You will lead ETL/ELT pipelines using Python, Spark, Databricks, and Azure Data Factory, and mentor engineers while aligning with enterprise data strategy.

You will collaborate with stakeholders, ensure data governance and security, and optimize pipelines across batch and streaming workloads in a hybrid Mexico-based team.

Qualifications

• Bachelor's degree or foreign equivalent in Computer Science or related field.
• 8+ years of IT experience, including pipeline design, support and development (Batch and Streaming) using Azure cloud services and Snowflake.
• Excellent assertive communication with users and stakeholders in English.
• Understanding of Agile methodologies.

Responsibilities

• Monitor and respond to alert emails regarding execution failures across Databricks, Azure Data Factory, or data quality gap reports within our processes.
• Proactively respond to user questions on team Zoom channels, capture issues in our processes, and implement necessary fixes.
• Work extensively with Azure Cloud Services, Azure Data Factory, Databricks, Python, and Spark, snowflake including handling user requested break-ins such as issue investigation, missing data analysis and remediation of jobs or pipelines that did not run according to their pre-configured schedules.

Skills

Python

Spark

ETL/ELT

Azure cloud

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

Azure Data Factory

Snowflake

Git

Job description

Infosys is seeking a Lead Azure Data Engineer to drive architecture and design of scalable data platforms on Azure. You will lead ETL/ELT pipelines using Python, Spark, Databricks, and Azure Data Factory, and mentor engineers while aligning with enterprise data strategy.

You will collaborate with stakeholders, ensure data governance and security, and optimize pipelines across batch and streaming workloads in a hybrid Mexico-based team.