Applied Materials Software Engineer 

India, Karnataka 

716709131

What We Offer

Requires in-depth knowledge and experience in data engineering practices. Uses modern data stack best practices and knowledge of internal or external business issues to improve data solutions. Solves complex data integration problems; takes a new perspective using existing solutions. Works independently, receives minimal guidance. Acts as a resource for colleagues with less experience.

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines using modern ETL/ELT tools and frameworks
• Implement efficient data models and warehousing solutions to support business intelligence initiatives
• Write high-quality, documented code for data integration, transformation, and automation
• Design and optimize database schemas and query performance
• Develop and maintain data quality monitoring systems and procedures
• Implement data governance practices and ensure data security compliance
• Troubleshoot complex data pipeline issues and performance bottlenecks
• Collaborate with stakeholders to gather requirements and translate them into technical specifications
• Create and maintain documentation for data flows, models, and processes
• Build reusable code and libraries for future use

Technical Skills Required:

• Strong programming skills in Python, SQL, and data processing frameworks
• Experience with modern data warehouse platforms (Snowflake, Redshift, BigQuery)
• Proficiency in ETL/ELT tools (Apache Airflow, dbt, or similar)
• Knowledge of big data technologies (Spark, Hadoop ecosystem)
• Experience with version control systems (Git) and CI/CD practices
• Understanding of data modeling concepts and best practices
• Familiarity with business intelligence tools (Tableau, Power BI, or similar)
• Demonstrates conceptual and practical expertise in data engineering and basic knowledge of related disciplines
• Strong understanding of data architecture principles and best practices

Business Expertise:

• Has knowledge of data integration best practices and how they integrate with other systems
• Understands business requirements and can translate them into technical solutions
• Awareness of industry trends in data engineering and analytics

Leadership:

• Acts as a resource for colleagues with less experience
• May lead small to medium-sized data projects
• Mentors junior team members on data engineering practices

Problem Solving:

• Solves complex data integration and pipeline problems
• Takes a new perspective on existing solutions
• Exercises judgment based on the analysis of multiple sources of information
• Optimizes data flows and improves system performance

Impact:

• Impacts data-driven decision making across the organization
• Influences data architecture and engineering practices
• Works within broad guidelines and policies to implement robust data solutions

Interpersonal Skills:

• Explains technical concepts and data solutions to non-technical stakeholders
• Works to build consensus across teams
• Collaborates effectively with cross-functional teams
• Communicates data quality and pipeline issues clearly