Databricks Data Engineer

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