Top Data Analyst Jobs in Delhi

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.

Analyze courier supply and dispatch operations across onboarding, retention, availability, earnings, assignment, acceptance, and delivery success. Build reliable metrics, dashboards, and self-service reporting; identify root causes of performance changes; evaluate experiments, incentives, and product initiatives; validate data quality; and translate findings into recommendations for product, operations, and leadership.

Analyzes, transforms, validates, and manages SAP and business data in cloud environments. Develops reports, dashboards, and analytical solutions; performs data quality, cleansing, reconciliation, migration, and troubleshooting activities. Collaborates with business stakeholders and SAP functional and technical teams to define requirements, resolve reporting issues, document processes, and present actionable insights.

Analyze structured and unstructured data to identify trends, build dashboards and reports, define KPIs, validate data quality, and provide actionable recommendations. The role requires SQL, BI visualization, Excel, relational database experience, and collaboration with business and technical stakeholders. Preferred skills include Python, ETL and data warehousing, cloud platforms, statistical analysis, forecasting, machine learning concepts, and Agile experience.

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.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality, data discoverability, testing, feature releases, quality standards, and customer-focused insights. Uses scientific expertise, Python and Bash scripting, databases, cloud platforms, and AI-enabled testing workflows to improve data integrity and platform performance. Coordinates stakeholders, documents work, presents findings, and drives cross-functional projects.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality evaluation, data discoverability, testing, feature releases, customer-focused insights, and quality standards. Uses scientific expertise, data analysis, scripting, databases, cloud platforms, and AI-enabled testing workflows to improve platform reliability and relevance. Coordinates stakeholders, advances projects, documents work, and presents recommendations for product and data improvements.

Own analytics and measurement for HighLevel’s AI product portfolio. Define instrumentation standards, feature-level KPIs, adoption and quality metrics, and rigorous approaches for evaluating nondeterministic AI features. Partner with experimentation leaders, PMs, engineering, and Analytics Engineering to close data gaps, influence product roadmaps, establish canonical metrics, and mentor future analysts.

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.

Provides research and data-analysis support to economists focused on monetary economics and financial markets in Asia and the Pacific. Responsibilities include preparing publications and presentations, collecting and validating data, reviewing methodologies, developing databases, identifying new data sources, applying advanced analytics, and collaborating on innovation projects. The role bridges economics, data, and technology in a multicultural international environment.

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 hosted e-discovery platforms by loading and transforming data, creating databases and user accounts, configuring permissions, investigating client technical inquiries, and assisting customers through phone, email, and screen sharing. The role also manages internal service-level tasks, collaborates with Engineering, IT, Application, and Project Management teams, and provides platform walkthroughs when needed.

The Senior Data Analyst will build and operate the data backbone for Operations by consolidating, cleaning, analyzing, and visualizing data across units and schemes. The role produces reliable dashboards and reports, identifies risks, anomalies, and performance gaps, validates data with Operations teams, and ensures reporting reflects actual conditions. Hands-on expertise in Python, MySQL, SQL, and Power BI is required, along with analytical independence and sound judgment.

Maintains taxonomies, business glossaries, classification hierarchies, metadata, and testable data rules in a governance platform. Analyzes rule libraries for conflicts, duplicates, and coverage gaps; supports reference data certification, request triage, documentation, governance reporting, and stakeholder enablement. The role ensures governed artifacts are accurate, traceable, auditable, and operationalized across data quality and classification workflows.

Own marketing database operations for EMEA, including audience segmentation, data quality, privacy compliance, and performance analysis. Translate complex marketing data into actionable insights, recurring reports, and segmentation models. Partner with Marketing Operations, IT, and Data teams to improve database accuracy and processes, while enabling stakeholders through clear communication and training. The role requires strong SQL, marketing technology, project ownership, and business-facing analytical skills.

Conduct a three-hour interactive online training session on qualitative data analysis, including thematic and content analysis, coding, codebook development, identifying themes, transcript and focus group analysis, practical case studies, and qualitative analysis tools.

Build, tune, deploy, and validate machine learning models for fraud detection, risk scoring, and customer analytics in the BFSI and credit card domain. The role involves performance and stability testing, documentation, troubleshooting data and model issues, communicating findings to varied stakeholders, managing analytical projects independently, and staying current with emerging AI and ML techniques.

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.

Own business data as a trusted source of truth by building metrics, dashboards, reporting systems, and analytical frameworks. Analyze complex datasets with advanced SQL, define KPIs, identify trends and opportunities, and solve ambiguous business problems. Partner with business, marketing, finance, and other teams to translate analysis into clear recommendations and data-driven decisions.

Build, maintain, and optimize enterprise data platforms using Databricks, Spark, cloud technologies, and DevOps practices. Develop CI/CD pipelines, support cloud migration and automation, monitor platform health, troubleshoot incidents, and ensure reliable, secure, and scalable data services. Participate in rotational production support across global time zones, perform root cause analysis, maintain operational documentation, and collaborate with engineering, architecture, product, and business teams.

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.

Lead oncology patient-journey, line-of-therapy, brand, HEOR, and healthcare resource utilization analytics using structured claims and patient data. Build SQL pipelines, perform exploratory and statistical analyses, ensure data governance and reproducibility, and translate findings into dashboards and client-facing insights. Collaborate with HEOR, market access, strategy, and engineering teams while independently managing project delivery and stakeholder communication.

Lead oncology analytics projects involving patient journeys, line-of-therapy mapping, claims data, brand analytics, and HEOR/HCRU studies. Build SQL pipelines and use Python and Excel to analyze structured healthcare datasets, ensure data integrity, and deliver reproducible insights. Develop dashboards and client presentations while collaborating with HEOR, market access, strategy, and engineering teams. Independently manage project delivery and stakeholder communication.

Develop and execute SAP data conversion programs for implementation projects. Perform legacy-to-SAP field mapping, transformation, and loading of master and transactional data using BODS, BAPIs, and IDOCs. Create validation and reconciliation reports, troubleshoot conversion defects, support testing and Go-Live activities, prepare technical documentation, and identify automation opportunities.

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.