We are looking for an experienced Senior Data Engineer with strong expertise in PySpark and Python to join our Data Engineering team supporting enterprise-scale Data & Analytics initiatives. The ideal candidate will have hands-on experience in building scalable ETL pipelines, data marts, and production-grade data engineering solutions across structured, semi-structured, and unstructured datasets.

The role requires strong technical capabilities in Big Data technologies, data warehousing, data analysis, software engineering best practices, and end-to-end SDLC ownership. Candidates with banking or financial services domain experience will be highly preferred.

Requirements

Key Responsibilities

• Design, develop, and maintain scalable ETL pipelines and data marts using PySpark and Python.
• Build robust, maintainable, and production-ready data engineering solutions.
• Perform end-to-end SDLC activities including development, UAT support, bug fixes, production deployments, and post-production support.
• Work with large-scale structured, semi-structured, and unstructured datasets.
• Perform data analysis, cleansing, transformation, and feature engineering activities.
• Debug and optimize PySpark code and complex SQL queries for performance and scalability.
• Develop and maintain production-grade data pipelines using modern data engineering best practices.
• Collaborate with cross-functional teams to resolve dependencies and ensure timely project delivery.
• Participate in CI/CD implementation, testing, validation, and deployment activities.
• Ensure data quality, integrity, and consistency across enterprise data platforms.
• Work closely with technical and business stakeholders to understand data requirements and deliver scalable solutions.
• Contribute to technical documentation, engineering standards, and process improvements.

Required Technical Skills

Programming & Data Engineering

• Python (Expert level)
• PySpark (Expert level)
• ETL Pipeline Development
• Data Mart Development
• Data Warehousing Concepts
• End-to-End SDLC Experience

Big Data Technologies

• Apache Spark (PySpark)
• Hadoop
• MapReduce
• Hive
• Pandas

Database Technologies

• SQL
• NoSQL Databases
• Oracle SQL
• Oracle Query Optimization & Data Analysis

Data Engineering & Analytics

• Data Analysis
• Data Cleansing
• Data Linking
• Data Transformation
• Feature Engineering
• Imputation Techniques
• Data Validation

Workflow & Orchestration Tools

• Apache Airflow
• Oozie
• Jenkins Pipelines

Software Engineering & DevOps

• Git Version Control
• CI/CD Pipelines
• Testing & Validation of Data Pipelines
• Production Deployment & Support
• Software Engineering Best Practices

Development Tools

• Jupyter Notebook
• Git

Required Experience

• 5+ years of commercial experience in Data Engineering or related data-driven roles.
• Strong hands-on experience in building ETL pipelines and Data Marts.
• Proven experience in developing production-grade PySpark and Python solutions.
• Strong understanding of software engineering concepts and best practices.
• Experience working with large-scale data processing frameworks.
• Hands-on experience with production support, UAT activities, and deployment processes.
• Strong analytical and debugging capabilities for PySpark and SQL-based data solutions.
• Experience working within Agile delivery environments is preferred.

Preferred Domain Experience

• Banking & Financial Services (Highly Preferred)
• Digital Products
• Data & Analytics Platforms

Soft Skills & Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and interpersonal skills.
• Ability to communicate effectively with both technical and non-technical stakeholders.
• Strong ownership mindset and accountability for deliverables.
• Ability to work under pressure and effectively prioritize tasks.
• Strong collaboration skills with cross-functional teams.
• Ability to lead technical initiatives and drive delivery outcomes.
• Excellent verbal and written communication skills in English.

Nice to Have

• Banking domain experience.
• Experience working with enterprise-scale Data & Analytics platforms.
• Exposure to Agile methodologies and modern data engineering practices.
• Knowledge of production-grade data pipeline monitoring and optimization.