Data Engineer - ETL/PySpark

Data Pipeline Development & Operations :- Design, build, and operate scalable and reliable data pipelines on the Databricks platform- Develop end-to-end data workflows from ingestion through transformation to consumption- Implement robust error handling, monitoring, and alerting mechanisms- Ensure data pipeline reliability, performance, and maintainability- Optimize pipeline performance through efficient Spark job design and cluster configuration- Manage and orchestrate complex data workflows using Databricks Jobs and workflowsLegacy Code Modernization :- Refactor legacy code and data pipelines to PySpark for improved performance and scalability- Migrate traditional ETL processes to modern ELT patterns on Databricks- Assess existing codebases and identify opportunities for optimization and modernization- Ensure backward compatibility and data integrity during migration processes- Document refactoring approaches and create migration playbooks- Collaborate with stakeholders to minimize disruption during code transitionsData Engineering Excellence :- Implement data quality checks and validation frameworks- Design and maintain Delta Lake tables with appropriate optimization strategies- Develop reusable code libraries and frameworks for common data engineering tasks- Follow software engineering best practices including version control, testing, and CI/CD- Participate in code reviews and provide constructive feedback to team members- Troubleshoot and resolve data pipeline issues in production environmentsCollaboration & Knowledge Sharing :- Work closely with data architects, analysts, and business stakeholders- Collaborate with Infrastructure (Infra), Applications (Apps), and Cyber teams- Share knowledge and best practices with Team NCS- Mentor junior data engineers on PySpark and Databricks technologies- Document technical solutions and maintain comprehensive documentationEssential Technical Skills :- Data Engineering: Strong foundation in data engineering principles, ETL/ELT processes, and data pipeline design patterns- PySpark: Proven hands-on experience developing data pipelines using PySpark, including DataFrames API, Spark SQL, and performance optimization- Databricks Platform: Practical experience with Databricks workspace, cluster management, notebooks, and job orchestration- Workspace AI Agent: Knowledge of Databricks Workspace AI Agent capabilities and integration- Data Modelling: Experience implementing data models including dimensional modeling, data vault, or lakehouse architectures- Delta Lake: Understanding of Delta Lake features including ACID transactions, schema evolution, and optimization techniques- Python: Strong Python programming skills for data processing and automation 5+ years of relevant experience- Strong foundation in data engineering principles, ETL/ELT processes, and data pipeline design patterns- Proven hands-on experience developing data pipelines using PySpark, including DataFrames API, Spark SQL, and performance optimization- Min 2 to 3 yrs exp in Databricks Platform: Practical experience with Databricks workspace, cluster management, notebooks, and job orchestration- Experience implementing data models including dimensional modeling, data vault, or lakehouse architectures- Understanding of Delta Lake features including ACID transactions, schema evolution, and optimization techniques- Strong Python programming skills for data processing and automation- Experience with cloud platforms (Azure, AWS, or GCP) mandatory to have at least one certification- Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer ProfessionalNotice Period - Immediate to 30 days (ref:hirist.tech)