Job Title: Banking Data Analyst

Experience: 5-10 Years

Location: Pan India (Preferred: Bengaluru, Pune, Hyderabad)

Notice Period: Maximum 45 Days

Number of Open Positions: 10

Job Summary

We are looking for an experienced and analytical Banking Data Analyst with strong hands-on expertise in Python, PySpark, SQL, Advanced SQL, and Data Modeling. The ideal candidate should have a minimum of 5 years of experience in data analytics and must possess relevant experience in the banking sector.

The candidate should be highly analytical and solution-oriented, with the ability to work on complex business use cases, manage and transform large datasets, identify data anomalies, and ensure data accuracy and quality. This role requires strong experience in building analytical data pipelines, performing data analysis and transformation, and translating business requirements into scalable data-driven solutions.

Please note that this role is focused on Data Analytics. Profiles primarily aligned with AWS Data Engineering, Azure Data Engineering, or pure PySpark Data Engineering should not be considered.

Key Responsibilities

- Analyze large and complex banking datasets to identify trends, patterns, anomalies, and actionable business insights.
- Use Python, PySpark, SQL, and Advanced SQL to perform data extraction, transformation, analysis, and validation.
- Develop and maintain scalable data pipelines to support analytical and business reporting requirements.
- Perform data management and transformation activities using PySpark.
- Write, optimize, and troubleshoot complex SQL queries for data manipulation,



analysis, and reporting.
- Design and maintain analytical data models to support business use cases and decision-making.
- Understand business requirements and translate them into effective analytical solutions.
- Analyze data for inconsistencies, discrepancies, missing information, and quality issues.
- Perform data validation, reconciliation, and anomaly detection to ensure data accuracy and reliability.
- Work closely with business stakeholders and cross-functional teams to understand business problems and deliver data-driven solutions.
- Support banking analytics use cases across relevant business domains.
- Contribute to data governance, data controls, data privacy, and data quality initiatives.
- Follow version-control best practices using Git or similar tools.
- Build and enhance automated analytical pipelines and improve the efficiency of existing data processes.
- Document data logic, analytical methodologies, business rules, and technical solutions.
- Collaborate with technical and business teams to ensure the successful delivery of analytics initiatives.

Mandatory Skills
- 510 years of overall experience in Data Analytics / Data Analysis.




- Minimum 5 years of hands-on experience using Python for Data Analytics.
- Robust experience in PySpark for data management, transformation, and processing of large datasets.
- Strong hands-on experience in SQL and Advanced SQL, including data extraction, manipulation, optimization, and analysis.
- Strong understanding of Data Modeling concepts and analytical data structures.
- Mandatory experience working in the Banking domain.
- Strong analytical, problem-solving, and solutioning capabilities.
- Experience identifying data anomalies, discrepancies, inconsistencies, and data-quality issues.
- Ability to understand complex business use cases and translate them into scalable analytical solutions.
- Knowledge of version-control tools such as Git.
- Strong communication and stakeholder-management skills.

Highly Desired Skills
- Experience with Data Governance.
- Knowledge of Data Controls and Data Privacy.
- Strong experience in Data Quality, data validation, and reconciliation.
- Experience building and maintaining analytical data pipelines.
- Experience working with large and complex datasets.
- Strong stakeholder-management and business-engagement skills.

Good-to-Have Skills
- Experience with Prophecy.
- Experience with pipeline automation and workflow orchestration.
- Exposure to banking analytics use cases such as credit risk, lending, underwriting, collections, customer analytics, fraud analytics, or portfolio analytics.
- Experience working with data warehouses, data lakes, or enterprise analytical platforms.

📌 Banking Data Analyst (Bengaluru)
🏢 Orcapod Consulting Services
📍 Bengaluru