Senior Data Engineer – Databricks

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

Skillset-(Must have)

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