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HCLTech

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Data Engineer

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Summary

Designs, builds, and maintains scalable batch and real-time data pipelines and curated datasets supporting Enterprise Credit Risk (lending decisioning, portfolio risk) using Databricks, Apache Spark/PySpark, Hadoop, SQL, and Python on cloud lakehouse platforms, with data quality, monitoring, CI/CD, and mentoring duties.

  • Design, develop, test, and maintain scalable data pipelines and data products supporting Enterprise Credit Risk initiatives.
  • Build and optimize batch and real-time data processing solutions using modern cloud and lakehouse technologies.
  • Partner with Risk, Product, Data Science, and Engineering teams to understand business requirements and translate them into technical solutions.
  • Develop reusable frameworks, components, and engineering patterns that improve development efficiency and platform consistency.
  • Implement data quality controls, monitoring, alerting, and observability capabilities to ensure data reliability and trustworthiness.
  • Support the migration and modernization of existing data assets and workloads into Databricks and cloud-native platforms.
  • Build and maintain curated datasets that support lending decisioning, portfolio risk management, and advanced analytics use cases.
  • Participate in architecture discussions and contribute technical recommendations for platform enhancements.
  • Troubleshoot production issues and drive root cause analysis to improve platform reliability and performance.
  • Follow engineering best practices for code quality, testing, deployment automation, security, and operational excellence.
  • Mentor junior engineers through code reviews, technical guidance, and knowledge sharing.
  • Continuously evaluate opportunities to improve scalability, performance, cost efficiency, and maintainability of data platforms.

Qualifications

  • Strong experience developing and maintaining enterprise-scale data engineering solutions.
  • Hands‑on experience with Databricks, Apache Spark, PySpark, Hadoop, SQL, and Python.
  • Experience building ETL/ELT pipelines and large-scale data processing applications.
  • Experience working with cloud-based data platforms and storage technologies.
  • Strong understanding of data modeling concepts and analytical data structures.
  • Experience implementing automated testing, monitoring, and data quality practices.
  • Experience with Data formats (Parquet, Avro, ORC).
  • Knowledge of CI/CD pipelines, Git‑based development workflows, and DevOps principles.
  • Experience with Workflow orchestration Tools like Airflow.
  • Strong analytical and problem-solving skills with the ability to work independently on complex technical challenges.
  • Effective communication skills with the ability to collaborate across technical and business teams.
  • Experience mentoring or supporting less experienced engineers.
  • Knowledge of Java Based application development is a huge Plus.

Skills

See also

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