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.

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

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