Senior Analyst

Kotak Mahindra Bank; Karnataka; Presencial; INR 1 500 000 - 2 700 000; Tempo integral

Resumo da oferta

Kotak Mahindra Bank is seeking a Senior Analyst in Business Analytics to translate business questions into analytics problems and to prioritize high-value use cases. You will work with product and business stakeholders to define outcomes and drive measurable impact.

You will analyse diverse data sources, build reusable analytics datasets, and develop scalable pipelines using Python, SQL, PySpark, and Airflow on AWS cloud.

Qualificações

• 5–10 years of relevant experience in business analytics, data analytics or data engineering.
• Strong hands-on Python, SQL and Spark experience in a banking/fintech context.
• Experience with ETL/ELT pipelines and Airflow DAGs; cloud data services preferred.

Responsabilidades

• Translate business questions into analytics problem statements with measurable outcomes.
• Analyse customer, transaction, product and engagement data to identify cross-sell and retention opportunities.
• Independently explore, transform and analyse large-scale datasets using SQL, Python and PySpark.
• Develop and monitor batch workflows using Apache Airflow and AWS services.
• Build segmentation, propensity, forecasting or rules-based solutions and support campaigns or dashboards.
• Communicate insights to Product, Business, Data Engineering and Governance teams.

Conhecimentos

Python

SQL

PySpark

Apache Spark

Pandas

Jupyter

Git

Data modelling

Performance tuning

Data-quality frameworks

Formação académica

Bachelor's or Master's in Mathematics/Statistics/Economics/CS/Engineering/Data Science or Management

Ferramentas

Apache Airflow

AWS S3

AWS EMR

AWS Redshift

AWS Glue

MWAA

ETL/ELT pipelines

Descrição da oferta de emprego

JD for the role of Senior Analyst Business Analytics

Location: Bangalore

Years of Experience: 5 - 10 Years

Role: Senior Analyst Business Analytics

Role Overview and Responsibilities:

Business problem solving and use-case prioritisation:

Partner with business and product stakeholders to translate business questions into structured analytics problem statements, define measurable outcomes, and prioritise high-value use cases. Apply knowledge of Retail Banking Liabilities, Investment and Insurance products, customer lifecycle, and channel behaviour.

Opportunity identification and insight generation:

Analyse customer, transaction, product and engagement data to identify cross-sell, deepening, retention and activation opportunities. Generate actionable insights to improve relationship value, revenue per customer, primary-banker penetration and cross-sell ratio.

Hands-on analytics and data engineering:

Independently explore, transform and analyse large-scale datasets using SQL, Python and PySpark. Build reusable analytical datasets, features and data pipelines; optimise Spark workloads; and apply sound coding, testing, documentation and version-control practices.

Pipeline orchestration and cloud delivery:

Develop, schedule and monitor reliable batch workflows using Apache Airflow. Work with AWS services such as S3, EMR, Redshift, Glue and MWAA to support scalable analytical processing, data movement, orchestration and production deployment.

Analytics solution development:

Build segmentation, propensity, classification, forecasting or rules-based solutions independently or in collaboration with data scientists. Validate outputs, explain methodology and limitations, and support implementation through campaigns, dashboards or downstream business processes.

Stakeholder management and communication:

Translate analysis into clear business narratives, recommendations and decision-ready presentations. Work closely with Product, Business, Data Engineering, Data Science, Technology and Governance teams to resolve dependencies and drive implementation.

Data quality, governance and controls:

Establish validation checks, reconcile source-to-output measures, troubleshoot pipeline failures, and ensure secure and compliant handling of customer data in line with internal policies and regulatory expectations.

Qualifications and Skills:

Education and experience: Bachelors or Masters degree in Mathematics, Statistics, Economics, Computer Science, Technology, Engineering, Data Science or Management from Tier 1/ 2 institutes. Typically 5- 10 years of relevant experience in business analytics, data analytics or data engineering, preferably in retail banking or financial services/ fintechs.

Core technical skills:

Strong hands-on proficiency in Python, SQL, PySpark and Apache Spark; experience building ETL/ELT pipelines and Apache Airflow DAGs; and practical exposure to AWS services including S3, EMR, Redshift, Glue and MWAA. Working knowledge of pandas, Jupyter, Git, data modelling, performance tuning and data-quality frameworks is expected.

Analytical capability:

Strong grounding in exploratory analysis, statistical methods, customer segmentation, experimentation, feature engineering and model evaluation. Ability to convert ambiguous business questions into logical analytical frameworks and measurable recommendations.

Delivery and ownership:

Manage assigned analytics initiatives from requirement clarification and data discovery through development, validation, implementation and post-production tracking. Proactively identify risks, communicate progress and deliver within agreed timelines.

Collaboration and mentoring:

Operate as a senior individual contributor, review analytical work, share technical best practices, and mentor junior analysts where required. Demonstrate curiosity, structured problem solving, attention to detail and a strong bias for business impact.