Sr. Data Engineer - PySpark (Banking)

We are looking for an experienced Senior Data Engineer with strong expertise in PySpark, Python, and Informatica BDM development to support large-scale Data & Analytics initiatives within an enterprise banking environment. The ideal candidate should possess hands-on experience in building and maintaining robust ETL pipelines, data marts, and scalable data processing solutions across structured, semi-structured, and unstructured data sources.

The role requires strong analytical capabilities, end-to-end SDLC experience, and the ability to work closely with cross-functional teams throughout development, testing, deployment, and production support activities.

Requirements

Key Responsibilities

• Design, develop, and maintain scalable ETL pipelines and Data Mart solutions.
• Develop high-performance data processing solutions using PySpark and Python.
• Perform end-to-end SDLC activities including development, UAT support, bug fixing, production deployment, and post-production support.
• Build and optimize data transformation pipelines for large-scale enterprise data platforms.
• Perform data analysis, code debugging, and performance tuning across PySpark and SQL-based solutions.
• Collaborate with business, analytics, and engineering teams to understand and implement data requirements.
• Ensure data quality, integrity, scalability, and reliability across data pipelines.
• Work with structured, semi-structured, and unstructured datasets in enterprise environments.
• Participate in code reviews and implement software engineering best practices.
• Support CI/CD implementation and data pipeline deployment activities.
• Troubleshoot production issues and implement effective resolutions.
• Contribute to technical documentation and knowledge-sharing initiatives.

Required Technical Skills

Programming & Big Data Technologies

• Python
• PySpark
• Apache Spark
• Informatica BDM (Big Data Management)
• Hadoop
• MapReduce
• Hive
• Pandas

Data Engineering & ETL

• ETL Pipeline Development
• Data Mart Development
• Data Warehousing Concepts
• Data Transformation & Processing
• Data Pipeline Optimization

Databases & Query Languages

• Oracle SQL
• SQL
• NoSQL Databases
• Strong analytical and query-writing skills

Tools & Platforms

• Jupyter Notebook
• Git / Version Control
• CI/CD Pipelines
• Testing & Validation Frameworks

Required Experience

• Minimum 5+ years of commercial experience in Data Engineering or Data Analytics projects.
• Strong hands-on experience in PySpark and Python-based ETL development.
• Experience building enterprise-grade ETL pipelines and Data Mart solutions.
• Strong experience in Informatica BDM development.
• Experience handling end-to-end SDLC activities including development, UAT, production deployment, and post-production support.
• Strong expertise in Oracle SQL and data analysis.
• Hands-on experience debugging PySpark code and optimizing data processing workflows.
• Experience working with production-grade data pipelines and large datasets.
• Strong understanding of software engineering principles and coding best practices.
• Experience working with Agile delivery environments.

Preferred Domain Experience

• Banking
• Financial Services
• Digital Products
• Data & Analytics Platforms

Nice to Have

• Experience working with enterprise data lake and big data ecosystems.
• Exposure to cloud-based data platforms.
• Experience working with CI/CD and automated data pipeline deployments.
• Knowledge of modern data engineering best practices and scalable architectures.

Daily Tech Stack

The selected candidate will work extensively with:

• Python
• PySpark
• Informatica BDM
• Apache Spark
• Jupyter Notebook
• Oracle SQL
• SQL & NoSQL Databases
• Hadoop Ecosystem (Hive, MapReduce)
• CI/CD Tools
• ETL & Data Warehousing Technologies

Functional Competencies

• Strong problem-solving and analytical skills.
• Excellent debugging and troubleshooting capabilities.
• Ability to work independently in a fast-paced Agile environment.
• Strong ownership mindset and attention to detail.
• Effective stakeholder communication and collaboration skills.
• Ability to manage multiple priorities and production support activities.