We are looking for a highly skilled Senior Data Engineer to contribute to the engineering, modernization, and continuous evolution of data processing platform on Databricks on AWS. While supporting the transition from the legacy Cloudera Hadoop platform to Databricks on AWS, this role will continue to play a key part in enhancing performance, simplifying pipelines, and delivering new capabilities on the Databricks platform over the long term.
The ideal candidate is a strong hands‑on Spark engineer with solid design experience, capable of contributing to architectural decisions while leading complex implementation and optimization efforts.
Responsibilities:
1. Platform Engineering & Modernization
Refactor and modernize existing Spark pipelines to Databricks native architectures
Eliminate legacy Hadoop dependencies and adopt cloud native AWS patterns
Enhance and extend existing processing logic using optimized Spark (ScalaSpark) on Databricks
2. Databricks Native Development
Build and optimize solutions using Databricks features, including Delta Lake, Databricks Workflows for orchestration and Auto scaling and job clusters
3. Design & Solution Engineering
Contribute to low and mid level architecture and design
Translate high level architecture into detailed technical designs
Define data models, pipeline patterns, and reusable components
Ensure solutions are scalable, maintainable, and production ready
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:
8+ years in data engineering or distributed systems
Strong expertise in Apache Spark (ScalaSpark/ 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 and equivalent experience
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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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