Senior Data Engineer — Databricks & Cloud Data Pipelines

HCL Technologies Limited; México; On-site; MXN 900,000 - 1,300,000; Full time

Job summary

HCL Technologies Limited is seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines for enterprise credit risk initiatives. You will work across Risk, Product, Data Science, and Engineering teams to translate requirements into robust cloud-native data solutions.

This role emphasizes real-time and batch processing with Databricks, Spark, PySpark, and Python, plus data modeling and automated data quality practices.

Qualifications

• Strong experience building enterprise-scale data engineering solutions.
• Hands-on with Databricks, Spark, PySpark, Hadoop, SQL and Python.
• Experience with cloud data platforms and storage technologies.
• Knowledge of data modeling and analytical data structures.
• Experience implementing automated testing, monitoring and data quality practices.
• Familiarity with CI/CD pipelines and Git-based workflows.

Responsibilities

• Design, develop, test, and maintain scalable data pipelines and data products for enterprise risk initiatives.
• Build and optimize batch and real-time data processing using cloud and lakehouse technologies.
• Collaborate with Risk, Product, Data Science, and Engineering teams to translate requirements into solutions.
• Develop reusable data engineering components and patterns for platform consistency.
• Implement data quality controls, monitoring, and observability for data reliability.
• Mentor junior engineers through code reviews and knowledge sharing.
• Participate in architecture discussions and drive platform improvements.

Skills

Analytical skills

Problem solving

Mentoring

Independent work

Tools

Databricks

Apache Spark

PySpark

Hadoop

SQL

Python

Java

Airflow

Job description

HCL Technologies Limited is seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines for enterprise credit risk initiatives. You will work across Risk, Product, Data Science, and Engineering teams to translate requirements into robust cloud-native data solutions.

This role emphasizes real-time and batch processing with Databricks, Spark, PySpark, and Python, plus data modeling and automated data quality practices.