Top Data Analyst Jobs in Delhi

Lead SOX compliance and ICFR activities including internal controls design, risk assessment, audit coordination, governance reporting, operational effectiveness, and stakeholder communication.

Supports liquidity regulatory reporting controls by documenting data flows, business data elements, lineage, transformations, and controls from booking systems to reporting platforms. Performs SQL-based data analysis, test-case development, functional and business testing, quality remediation, root-cause analysis, requirements management, stakeholder coordination, and management reporting. Contributes to implementation activities, change control, documentation standards, and training junior team members.

Supports fundraising and donor management analytics during a six-month internship. Maintains donor and campaign databases, cleans and validates data, builds Excel trackers, dashboards, and reports, analyzes donor and campaign trends, and presents actionable findings to leadership. The intern also documents datasets and reporting tools and delivers a comprehensive final analysis report with strategic recommendations.

Analyze and interpret large healthcare datasets, build and maintain data pipelines and structures, collaborate with cross-functional teams to deliver actionable insights, and document data processes and analyses.

Analyze large healthcare datasets to extract insights, build data models, reports and dashboards, ensure data quality, collaborate with cross-functional teams, and present findings to stakeholders.

Analyzes D2C, marketplace, and quick-commerce channel profitability, including unit economics, margins, trade spend, discounts, returns, and write-offs. Performs budget variance analysis, builds MIS reports and dashboards, automates reporting, identifies margin leakage, and supports pricing, forecasting, inventory planning, and working capital optimization. Collaborates with Supply Chain, Sales, Operations, and Growth teams to provide financial insights and flag business risks.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Develop and deliver AI/ML solutions by building supervised and unsupervised models, deep learning systems, NLP and LLM applications. Responsibilities include LLM fine-tuning, prompt engineering, RAG, vector databases, transformer architectures, and AI agent deployment. The role requires partnering with business stakeholders, presenting technical findings, and continuously learning emerging AI technologies.

Perform gap analyses across trading data from multiple source systems, identify discrepancies, build automated reports using Python and SQL, create visualizations (Power BI or Python libraries), present findings to stakeholders, and coordinate with upstream/downstream teams to implement logic and document requirements.

Analyze operational data and processes, identify bottlenecks, design scalable solutions, automate workflows and reporting with Python and SQL, build dashboards and monitoring, perform root-cause analysis, document logic and collaborate with product, engineering, and ops teams to improve automation and data quality.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Gather, clean, and analyze data from multiple sources; build and maintain Power BI and Excel dashboards; run SQL queries and Python scripts for automation; collaborate with cross-functional teams to interpret trends and communicate insights; create documentation and improve reporting processes.

Perform exploratory analysis, build dashboards and reports (Tableau), write SQL queries, use Python for data cleaning and predictive models, collaborate with stakeholders, communicate insights, and maintain data models, pipelines, and quality checks.

Design, develop, and support analytics solutions, dashboards, reports, and data models. Prepare and transform data, ensure data quality and governance, collaborate with stakeholders, leverage AI to accelerate delivery, and drive process improvements to enable data-driven decisions.

Classify merchant and product data into accurate channels, brands, and categories using a custom data-cleanup tool. Research merchants, brands, and categories to validate classification accuracy; learn and apply new tool features; document and improve data hygiene processes; meet deadlines; and collaborate across teams while managing shifting priorities. Evening IST availability is required for meetings with the US-based team, along with asynchronous Slack communication.

Supports client operational and analytical processes by executing procedures, validating deliverables, identifying process improvements, coordinating stakeholders, testing updated workflows, documenting operational models, researching issues, and investigating client questions. Collaborates with senior analysts, delivery leads, engineering teams, and internal stakeholders to provide accurate, timely client results across multiple tasks and time zones.

Supports client operations through data analysis, process execution, quality validation, reporting, secondary research, and investigation of client questions. The role coordinates stakeholders across time zones, documents and tests operational procedures, identifies process improvements, and collaborates with engineering and delivery teams to ensure accurate, timely client deliverables.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Analyze large-scale on-chain data to detect anomalous transactions, develop detection mechanisms and risk monitoring frameworks, apply supervised and unsupervised ML, identify malicious accounts via community-discovery algorithms, and design integrated on-chain/off-chain prevention and control systems.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Support data integrations and quality by checking ingested/system-generated data for anomalies, building and running QA checks and automations with Python and SQL, assisting on customer data queries and dashboards, and documenting findings to improve prediction reliability.

Early-career Data Analyst who designs and owns core analytics, dashboards, and data pipelines. Analyze transaction, customer, and credit data to inform credit, fraud, AML, portfolio performance, unit economics, and growth. Build automations and workflows with SQL and Python, partner with cross-functional teams, and own compliance reporting and durable analytical systems.

Build and maintain logistics data pipelines, dashboards, and reports; validate and cleanse datasets; translate business requirements into scalable data models; perform analysis to identify trends and root causes; align metrics with global KPIs and governance; prioritize analytics demand and partner with cross-functional teams to improve visibility and logistics performance.

Design, build, and govern HSW data solutions and analytics; deploy AI and automation into HSW processes; produce Power BI dashboards and regular reports; support Cority operations; drive data strategy, governance, and actionable insights to improve safety and operations.

Drive end-to-end product analytics for user growth and product strategy: define metrics, design and analyze A/B tests, handle PB-scale behavioral and transactional data, build dashboards, predictive models, and deliver actionable insights to product and business stakeholders.