Databricks Data Engineer
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Summary
Design, build, and operate scalable data pipelines on Databricks, modernize legacy ETL code to PySpark, and mentor engineers — core stack is Databricks, PySpark, Delta Lake, and Python.
Essential 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
Additional Technical Skills SQL proficiency for data querying and transformation Experience with cloud platforms (Azure, AWS, or GCP) Understanding of data governance and security best practices Knowledge of streaming data processing (Structured Streaming) Familiarity with DevOps practices and CI/CD pipelines Experience with version control systems (Git) Understanding of data quality frameworks and testing methodologies
Professional Experience Minimum 8 years in data engineering or related roles At least 2-3 years of hands-on experience with Databricks platform Proven track record of refactoring legacy code to modern frameworks Experience building and maintaining production data pipelines at scale Background working across multiple data sources and formats Experience in agile development environments
Required Certifications Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer Professional
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 workflows
Legacy 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 transitions
RESPONSIBILITIES:
Data 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 environments
Collaboration & 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 documentation
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
Additional Technical Skills SQL proficiency for data querying and transformation Experience with cloud platforms (Azure, AWS, or GCP) Understanding of data governance and security best practices Knowledge of streaming data processing (Structured Streaming) Familiarity with DevOps practices and CI/CD pipelines Experience with version control systems (Git) Understanding of data quality frameworks and testing methodologies
Professional Experience Minimum 8 years in data engineering or related roles At least 2-3 years of hands-on experience with Databricks platform Proven track record of refactoring legacy code to modern frameworks Experience building and maintaining production data pipelines at scale Background working across multiple data sources and formats Experience in agile development environments
Required Certifications Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer Professional
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 workflows
Legacy 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 transitions
RESPONSIBILITIES:
Data 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 environments
Collaboration & 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 documentation