Data Engineer - Python/ETL/SQL

Description :

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.

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.

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 :

• Mandatory to have at least one certification : Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer Professional.

Additional Certifications (Preferred) :

• Databricks Certified Associate Developer for Apache Spark.
• Cloud platform certifications (Azure Data Engineer Associate, AWS Certified Data Analytics, or Google Cloud Professional Data Engineer).
• Relevant data engineering or big data certifications.

Soft Skills :

• Strong problem-solving and analytical thinking abilities.
• Excellent communication skills to explain technical concepts clearly.
• Ability to work collaboratively in cross-functional teams.
• Self-motivated with strong attention to detail.
• Adaptable to changing priorities and technologies.
• Client-focused mindset with commitment to quality delivery.