Senior Data Engineer

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

• Databricks Certified Associate Developer for Apache Spark (Mandatory)
• 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