Senior Databricks Apache Spark - SQL Engineer- Vice President

Citi; Pune District; On-site; INR 400,000 - 650,000; Full time

Job summary

Citi is seeking a Senior Databricks Engineer to drive modernization of data processing on AWS Databricks, aligning legacy Cloudera Hadoop transitions with scalable cloud-native patterns. The role focuses on hands-on Spark development, architectural input, and delivering optimized pipelines using Delta Lake, Databricks Workflows, and auto scaling.

You will collaborate with global teams to ensure production-ready solutions.

Qualifications

• 12+ 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 or refactoring of legacy data platforms into cloud-based architectures.
• Spark performance tuning and large-scale batch optimization.

Responsibilities

• Refactor Spark pipelines to Databricks native architectures.
• Eliminate legacy Hadoop dependencies and adopt cloud-native AWS patterns.
• Enhance processing logic using optimized Spark on Databricks (JavaSpark / PySpark).
• Build and optimize solutions using Databricks features (Delta Lake, Workflows, auto scaling).
• Contribute to low and mid-level architecture and detailed designs; define data models and reusable components.
• Ensure solutions are scalable, maintainable, and production-ready.
• Follow Databricks and Spark engineering standards; write clean, modular code and contribute to libraries.
• Collaborate with senior architects, platform teams, and DevOps engineers; provide guidance.
• Develop unit, integration, and data validation tests; support production releases.

Skills

Spark (Java/PySpark)

Databricks on AWS

Delta Lake

SQL

AWS services

Distributed data processing

Performance tuning

Education

Bachelor's degree or equivalent

Job description

Job Description -

We are looking for a highly skilled Senior Databricks 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 (JavaSpark / PySpark) on Databricks

1. Databricks Native Development

• Build and optimize solutions using Databricks features, including Delta Lake, Databricks Workflows for orchestration and Auto scaling and job clusters

1. 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

1. Performance Optimization & Simplification

• Analyze, improve Spark job performance and simplify complex or over engineered pipelines into standardized, efficient patterns

1. 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

1. 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

1. Testing & Quality Assurance

• Develop unit, integration, and data validation tests
• Support production releases and post deployment validation

Qualifications:

Core Technical Skills

• 12+ 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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