Lead Data Engineer

Job 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 - 14 years in data engineering or related roles.

- At least 4 - 8 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.

(ref:hirist.tech)