Role & responsibilities

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 5 years (Relevant Experience) 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
Qualified

📌 Data Engineer (Maharashtra)
🏢 Swift Staffing
📍 Maharashtra