Senior Databricks Apache Spark - SQL Engineer- Vice President

Citi; Pune District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Citi is seeking a Senior Databricks Engineer in Pune to modernize the data platform on AWS Databricks, replacing legacy Cloudera Hadoop. The role focuses on performance, scalable pipelines, and designing reusable components across distributed systems.

Ideal candidates will be hands-on Spark experts with experience in Databricks on AWS, Delta Lake, and architectural decision-making, contributing to long-term cloud-native data processing capabilities.

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.

Responsibilities

• Platform Engineering & Modernization: Refactor and modernize Spark pipelines to Databricks native architectures and eliminate Hadoop dependencies.
• Databricks Native Development: Build and optimize solutions using Databricks features like Delta Lake and workflows.
• Design & Solution Engineering: Translate high-level architecture into detailed technical designs and reusable components.
• Performance Optimization & Simplification: Improve Spark performance and simplify pipelines into standardized patterns.
• Engineering Standards & Best Practices: Write clean, modular code and contribute to shared frameworks.
• Collaboration & Stakeholder Engagement: Work with senior architects, platform teams, and DevOps.
• Testing & Quality Assurance: Develop unit and integration tests and support production releases.

Skills

12+ years in data engineering

Apache Spark

Databricks on AWS

Delta Lake

SQL

AWS services

Education

Bachelor's degree or equivalent

Tools

Databricks

AWS

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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