Work with cutting-edge big data platforms (e.g., Databricks, Apache Spark) at large scale, pushing the boundaries of data processing and model enablement. Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise data platforms. Develop high-performance data processing jobs using PySpark/Spark, Python on data platforms such as cloudera and databricks. Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns. Contribute to CI/CD for data workflows (testing, code reviews, deployment automation), promoting engineering best practices and maintainable codebases. Partner with Product Managers to develop a deep understanding of users and use cases and apply that knowledge to scoping and building new modules and features Ideal Candidate Qualifications: Strong hands-on experience in data engineering building production-grade pipelines on big data platforms (Hadoop ecosystem and cloud data platforms - databricks). High proficiency in using Python, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL to build Big Data products. Hands-on experience with cloud data platforms such as databricks, snowflake (databricks preferred). Experience with orchestration/integration tools such as Apache Airflow, Apache NiFi, or Talend. Working knowledge of DevOps/CI-CD practices: version control (Git), automated testing, release pipelines, and observability. Strong problem-solving skills with the ability to debug complex data issues and communicate clearly with technical and non-technical stakeholders. Experience developing Java based applications is an added advantage.

Key Responsibilities

- Work with cutting-edge big data platforms (e.g., Databricks, Apache Spark) at large scale, pushing the boundaries of data processing and model enablement.
- Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise data platforms.
- Develop high-performance data processing jobs using PySpark/Spark, Python on data platforms such as cloudera and databricks.
- Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns
- Contribute to CI/CD for data workflows (testing, code reviews, deployment automation), promoting engineering best practices and maintainable codebases.
- Partner with Product Managers to develop a deep understanding of users and use cases and apply that knowledge to scoping and building new modules and features
- Idóneo Candidate Qualifications:




- Strong hands-on experience in data engineering building production-grade pipelines on big data platforms (Hadoop ecosystem and cloud data platforms - databricks).
- High proficiency in using Python, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL to build Big Data products.
- Hands-on experience with cloud data platforms such as databricks, snowflake (databricks preferred)
- Experience with orchestration/integration tools such as Apache Airflow, Apache NiFi, or Talend.
- Working knowledge of DevOps/CI-CD practices: version control (Git), automated testing, release pipelines, and observability.
- Strong problem-solving skills with the ability to debug complex data issues and communicate clearly with technical and non-technical stakeholders.
- Experience developing Java based applications is an added advantage.

Skill Requirements

- Work with cutting-edge big data platforms (e.g., Databricks, Apache Spark) at large scale, pushing the boundaries of data processing and model enablement.
- Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise data platforms.
- Develop high-performance data processing jobs using PySpark/Spark, Python on data platforms such as cloudera and databricks.
- Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns
- Contribute to CI/CD for data workflows (testing, code reviews, deployment automation), promoting engineering best practices and maintainable codebases.
- Partner with Product Managers to develop a deep understanding of users and use cases and apply that knowledge to scoping and building new modules and features
- Ideal Candidate Qualifications:
- Strong hands-on experience in data engineering building production-grade pipelines on big data platforms (Hadoop ecosystem and cloud data platforms - databricks).
- High proficiency in using Python, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL to build Big Data products.
- Hands-on experience with cloud data platforms such as databricks, snowflake (databricks preferred)
- Experience with orchestration/integration tools such as Apache Airflow, Apache NiFi, or Talend.
- Working knowledge of DevOps/CI-CD practices: version control (Git), automated testing, release pipelines, and observability.




- Strong problem-solving skills with the ability to debug complex data issues and communicate clearly with technical and non-technical stakeholders.
- Experience developing Java based applications is an added advantage.

Other Requirements

- Work with cutting-edge big data platforms (e.g., Databricks, Apache Spark) at large scale, pushing the boundaries of data processing and model enablement.
- Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise data platforms.
- Develop high-performance data processing jobs using PySpark/Spark, Python on data platforms such as cloudera and databricks.
- Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns
- Contribute to CI/CD for data workflows (testing, code reviews, deployment automation), promoting engineering best practices and maintainable codebases.
- Partner with Product Managers to develop a deep understanding of users and use cases and apply that knowledge to scoping and building new modules and features
- Ideal Candidate Qualifications:
- Strong hands-on experience in data engineering building production-grade pipelines on big data platforms (Hadoop ecosystem and cloud data platforms - databricks).
- High proficiency in using Python, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL to build Big Data products.
- Hands-on experience with cloud data platforms such as databricks, snowflake (databricks preferred)
- Experience with orchestration/integration tools such as Apache Airflow, Apache NiFi, or Talend.
- Working knowledge of DevOps/CI-CD practices: version control (Git), automated testing, release pipelines, and observability.
- Strong problem-solving skills with the ability to debug complex data issues and communicate clearly with technical and non-technical stakeholders.
- Experience developing Java based applications is an added advantage.

At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026totaled $14.8billion.

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📌 Technical Specialist (Ciudad de México)
🏢 HCL Technologies
📍 Ciudad de México