4. Performance Optimization & Simplification
Analyze, improve Spark job performance and simplify complex or over engineered pipelines into standardized, efficient patterns
5. Engineering Standards & Best Practices
Follow and contribute to Databricks and Spark engineering standards
Write clean, modular, and testable code
Contribute to shared frameworks, reusable libraries, and quality standards
6. Collaboration & Stakeholder Engagement
Work closely with senior architects, platform teams, and DevOps engineers
Provide technical inputs, troubleshooting support, and implementation guidance
Participate in design discussions and technical decision making
7. Testing & Quality Assurance
Develop unit, integration, and data validation tests
Support production releases and post deployment validation
Qualifications
Core Technical Skills
10+ years in data engineering or distributed systems
Strong expertise in Apache Spark (JavaSpark / PySpark), Databricks on AWS, and Delta Lake
Experience on SQL
Experience with AWS services and large‑scale distributed data processing
Modernization & Optimization Experience
Experience modernizing or refactoring legacy data platforms into cloud‑based architectures
Strong background in Spark performance tuning and large‑scale batch optimization
Design Capability
Ability to translate architecture into implementable designs
Understanding of data modeling and pipeline orchestration patterns
Behavioral Competencies
Strong problem‑solving mindset for complex distributed systems
Comfortable working in time‑bound, high‑impact environments
Proactive, accountable, and collaborative
Clear communication skills across global teams
Education
Bachelor’s degree/University degree or equivalent experience
Job Family Group
Technology
Job Family
Applications Development
Time Type
Full time
- Most Relevant Skills
- Please see the requirements listed above.
- Other Relevant Skills
- For complementary skills, please see above and/or contact the recruiter.
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