Data Analyst

We are looking for ambitious and analytical minds to join our Data team as a Data Analyst for Consumer Spend Research. This is not a conventional reporting role. You will analyse US consumer credit card spend data, research and classify brands and merchants, and map them accurately to create insights and analytical reports. Alongside that analytical work, you will spend a significant part of your time building and maintaining the pipelines that move and shape that data, working with senior data engineers, data scientists and analysts to turn raw transaction data into reliable, analysis-ready datasets.

The ideal candidate combines a genuinely critical eye for data with strong Python and SQL skills, curiosity about cloud data infrastructure, and comfort with modern development practices including AI-assisted coding and version control. You will be given real ownership early and supported by an experienced team.

Location: Kolkata (Rajarhat, New Town)

Experience Level : 1-3 years

Timing: Ability to work in the US Eastern time zone. This may be relaxed to half day IST and half day US EST, based on project needs.

Key Responsibilities:

Consumer spend research and analysis

• Analyse US consumer credit card spend data, identifying patterns, anomalies and variations that are not obvious at first glance.
• Research brands and merchants online and map transaction data to the correct entities with a high standard of accuracy.
• Conduct online research across search engines, directories and unconventional sources to resolve ambiguous or incomplete records.
• Produce clear, well-written analytical reports and insight summaries for client-side stakeholders.
• Perform exploratory analysis to investigate data discrepancies, identify trends and support decision-making by product and business teams.

Data engineering and pipeline work

• Build and maintain ETL and ELT scripts and data transformation logic in Python and SQL, under the guidance of senior engineers.
• Write clean, tested, well-documented Python for data ingestion, cleaning, validation and enrichment across multiple source systems.
• Support the development and monitoring of data workflows orchestrated with Apache Airflow, including troubleshooting failed runs.
• Work with AWS data services including S3, Glue, Athena and Redshift to move, store and query data at scale.
• Write and optimise complex SQL queries, and use regular expressions to filter, classify and clean large volumes of transaction data.
• Implement and run data quality checks, reconciliation routines and alerting so issues are caught before they reach downstream consume

Ways of working

• Use AI-powered coding assistants and IDE integrations such as Kiro to accelerate development, code review and documentation.
• Follow version control and code review practices using Git.
• Apply data governance, security and access-control standards consistently across all work.
• Document pipelines, data definitions, classification rules and analysis methods so the team can rely on and extend your work.

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Required Qualifications:

• Graduate in any discipline. A degree in Computer Science, Engineering, Statistics, Data Science, Mathematics or a related field is preferred, as is exposure to market research or data analytics.
• 1 to 3 years of professional experience in data analysis, data engineering, market research or a closely related role.
• Strong proficiency in Python for data work, including pandas, and the ability to write production-quality scripts rather than notebook-only code.
• Strong SQL skills, including joins, window functions, aggregations and query optimisation.
• Working knowledge of regular expressions for pattern matching and data classification. This is mandatory.
• Working exposure to AWS, covering services such as S3, Glue, Athena and Redshift.
• Understanding of ETL and ELT concepts, data modelling fundamentals and relational database design.
• Familiarity with Git and collaborative development workflows.
• Demonstrated ability to use AI-assisted development tools within modern IDEs for prototyping, code generation, testing and debugging.
• Software literacy: able to learn new programs quickly and comfortable navigating unfamiliar interfaces and expressions.
• A critical eye: able to think laterally to find sources of information that help distinguish variations in data that look identical at first glance.
• Online research capability: comfortable navigating web pages, search engines, directories and unconventional sources.
• Excellent reading and writing: can process and produce long or complex alphanumeric strings, both abstract and functional, with ease and accuracy.
• Excellent communication: can clearly explain how and why decisions were made, and can take and act on constructive feedback.
• Working knowledge of Microsoft Office, particularly Excel, Word and Outlook.
• Good written and spoken English, with the confidence to work directly with client-side stakeholders.

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Good to Have:

• Hands-on exposure to Apache Airflow or a comparable orchestration tool.
• Experience with data annotation, taxonomy building or other forms of data markup.
• Exposure to machine learning or LLM workflows, or to supporting data scientists with feature preparation.
• Prior experience working with US market data or financial transaction data.
• Residence within one hour of travel to the office.