NTT DATA Hiring Business Analyst Data Analytics in Bangalore – Full Job Details, Salary, Skills,

NTT DATA Hiring Business Analyst Data Analytics

Introduction

With data-driven decision-making upon the rise, companies are now banking more on analytics and business intelligence (BI) teams to transform raw data into actionable insights. NTT DATA, one of the major IT consulting and services players globally, has an active opening for a Business Intelligence Analyst (Data & Analytics) role in Bangalore, India — both for fresh graduates and professionals. The role bridges business and technology and provides a promising avenue for growth.

In this step-by-step guide, we will delve deep into what this role involves, what NTT DATA looks for, how you can prepare, and how you can be different. Whether you are a fresher looking to enter BI or an experienced data professional looking for your next step, this blog should act as a guide.

About NTT DATA & Its Analytics Vision

• Prior to getting started on the job, it is good to know the company and its analytics vision:
• NTT DATA is a multinational technology services and consulting company. It offers end-to-end solutions encompassing consulting, applications, data & artificial intelligence, cloud & infrastructure, and more.
• NTT DATA works towards empowering customers to transform and innovate. They invest in research and development and create analytics, AI, and data platforms.
• The “Data & Analytics” domain is integral to their strategy — helping clients leverage data to transform operations, optimize processes, gain insights, and drive better outcomes.
• In the careers listing, NTT DATA highlights advanced analytics, dashboards, data modelling, and reporting as core to this role.
• Hence, the role you are looking at is not just a typical BI role — it sits at the intersection of business, data, and technology.

Role Description: Business Intelligence Analyst (Data & Analytics)

• The Business Intelligence Analyst, Bangalore (R-136424) job posting defines this role as a role where the candidate contributes to transforming data into meaningful and actionable intelligence, creating reports and dashboards, and working with stakeholders.
• Here’s how you can see the top-level role:
• You bridge business and data: collecting requirements, knowing the business context, and then translating that into data models, visualizations, and insights.
• You enable decision-making through reports, dashboards, metrics, and insights.
• You provide data quality, accuracy, and relevance.
• You work with cross-functional groups — engineering, operations, business units, etc.
• Depending on project requirements, you might assist with ad hoc analyses, experimentation, or predictive modelling.
• The position is available to both freshers and experienced individuals, i.e., NTT DATA expects training, ramp-up, and mentoring as well as growth opportunities.

Key Responsibilities & Deliverables

Let’s break down in detail what you might be doing in this job — what your month or week might be like.

Requirement Gathering & Stakeholder Engagement

• Learning business request: You’ll sit down with business owners, product managers, operations leads, or domain stakeholders and learn what they need to do (i.e. “Show me trend in customer churn over time,” or “Where are we losing funnel conversions?”).
• Converting business into data needs: Business requests like “increase retention,” you translate that into metrics such as retention rate, churn score, cohort analysis, etc.
• Documenting needs: Employ standard templates — BRD (Business Requirements Document), URS (User Requirements Specification), user stories, acceptance criteria.
• Elaborating scope: Refine or reject scope if information is unavailable or impracticable, or redesign the ask in phases.

Data Analysis, Modeling & ETL

• Data extraction / collection: Extract data from internal systems (OLTP databases, CRM, ERP), external systems, APIs, logs, or third-party data sets.
• Data cleansing & transformation: Identify missing values, inconsistencies, outliers; normalize fields, standardize formats, transform data into analytics schema.
• Data modeling: Define dimension and fact schemas, star/snowflake designs, relationships, define aggregated tables.
• ETL pipelines: You can assist in designing or managing ETL (extract-transform-load) jobs, ensuring that the data moves correctly and reliably into the analytics environment.
• Performance optimization: Indexing, partitioning, caching, efficient query design.

Dashboarding, Reporting & Visualization

• Creating dashboards: Choose which KPIs to display, how to display them (charts, heatmaps, tables, filters), what interactivity to add.
• Implementation of tools: Utilize tools such as Power BI, Tableau, Looker, Qlik, or other tools to create and publish dashboards.
• Ad hoc reporting: Assist the business with on-demand reports, cut by region, period, product, user segment, etc.
• Alerts & notifications: Implement thresholds and triggers such that stakeholders are notified if measures go beyond certain limits.

Metrics Definition, Monitoring & Insights

• Define business metrics / KPIs: E.g. revenue growth, user retention, conversion rate, cost per acquisition, etc.
• Monitoring & benchmarking: Compare metrics over time, across cohorts, against targets or industry benchmarks.
• Interpretation & insights: Go beyond numbers — find correlations, root causes, anomalies, suggestions (e.g. “Why did revenue dip last month?”).
• Recommendations & business impact: Report conclusions and recommend actions (e.g. invest more in channel X, drop product Y, improve process Z).

Data Quality, Validation, Governance

• Validation checks: Verify correctness of data, sanity check, identify missing or incorrect data.
• Data reconciliation: Compare between source systems, resolve differences.
• Governance & documentation: Keep data dictionaries, metadata, definitions, lineage, and document assumptions.
• Access controls & security: Provide only authorized users with access, and data privacy or regulatory compliance.

Cross-Functional Collaboration & Support

• Data engineering liaison: Collaborate with data engineers to construct pipelines, merge data sources, have the data you need come available and be modelled correctly.
• Domain team coordination: e.g., marketing, operations, product, finance — to learn about domain logic and obtain contextual insight.
• Support testing & rollout: Check dashboards and reports, assist in user acceptance testing (UAT), collect feedback.
• Continuous improvement: Track usage of dashboards, collect feedback, refine and improve.

Ad Hoc Analysis & Support

• Sporadic analysis requests: e.g. campaign effectiveness, user segmentation, cross-sell opportunities.
• Root cause or diagnostic investigations: e.g. user drop-offs, revenue shortfall, sudden anomalies.
• Forecasting & predictive modeling (skilled-dependent): Develop or assist models (regression, classification) to predict demand, churn, etc.
• Scenario analysis: “What-if” modeling to look at results under varying assumptions.

Needed Skills, Competencies & Tools

In order to fulfill the above duties competently, here’s what NTT DATA will probably be searching for in applicants.

NTT DATA Hiring Business Analyst Data Analytics

Analytical / Technical Skills

• SQL / query languages: Necessary. You will be querying relational databases regularly to change or accumulate data.
• Data modeling & database design: Knowing to create star/snowflake schemas, dimensional modeling, normalization, indexing, key constraints.
• ETL / data pipelines: Knowing how data is ingested, transformed, and loaded — working with pipelines, scheduling, error handling, incremental loads.
• Visualization / BI tools: Competency in at least one dashboard or visualization tool (Tableau, Power BI, Qlik, Looker, etc.).
• Programming / scripting (optional / benefit): Python, R, or equivalent for more complex transformation, automation, analysis.
• Statistical & analytical techniques: Simple descriptive statistics, trend analysis, correlation, segmentation, A/B test analysis.
• Management of large datasets: Familiarity with big data systems (Spark, Hadoop, etc.) or cloud data warehouses (Snowflake, Redshift, BigQuery) is a bonus.
• Data integration / APIs: Retrieval of data from APIs, third-party systems, or cloud services.
• Data quality tools / techniques: Methods for data validation, cleansing, transformation logic.
• Version control & documentation: Utilizing Git or an equivalent, and keeping documentation up to date.

Soft Skills & Domain Knowledge

• Stakeholder communication: Capacity to comprehend business requirements, pose clarifying questions, and communicate insights to non-technical stakeholders.
• Problem-solving mindset: Gaps detection, eliminating the cause, conceptualizing the solution.
• Domain knowledge: Knowledge of the field you operate in (e.g. finance, retail, operations, supply chain) assists you in interpreting data and recommending changes more effectively.
• Detail accuracy & orientation: Error in BI reports can lead to incorrect decisions — attention to detail is essential.
• Adaptability & learning agility: This is a fast-evolving field — willing to learn new tools, techniques, and adapting to changing requirements is key.
• Teamwork & collaboration: You’ll work with engineers, product teams, business units, QA, etc.
• Time management & prioritization: Handling multiple dashboard requests, analyses, and stakeholder demands.

Educational Qualification & Experience

• Bachelor’s degree in any relevant field is a base requirement. Fields such as computer science, data science, statistics, mathematics, business analytics, or engineering are advantageous.
• Freshers & experienced both eligible: The posting clearly states that freshers and experienced individuals can apply (which means the company might offer training or ramp-up support).
• Prior project or internship exposure: For freshers, exposure to working on analytics, dashboards, or data projects during studies or internships can be a differentiating point.
• Domain experience (for seniors): Industry experience in areas related to the client (retail, supply chain, finance, etc.) is advantageous.
• Certifications (Optional but helpful): BI tool certifications (Tableau, Power BI), data analytics, or cloud platform certifications can enhance your profile.

Salary Benchmarks & Career Growth

Salary Estimates

• As per Indeed, the bench mark average pay for a Business Intelligence Analyst at NTT DATA in Karnataka is approximately ₹ 7,64,051 per year (which is a benchmark average of previous and current postings).
• The salary varies from low (~ ₹ 2,08,000) to high (~ ₹ 16,66,000) based on experience, field, seniority, and performance.
• For freshers, the beginning salary could be lower — probably between ₹ 3–6 lakhs per annum, varying with skills, location, and negotiations.

How to Prepare Yourself for This Role (for Freshers & Experienced)

This is how you can prepare yourself for the NTT DATA BI role.

Learning & Upskilling

• Learn SQL — this is a given. You need to be able to code complex joins, subqueries, window functions, aggregations, CTEs etc.
• Choose a BI tool & construct projects — e.g., construct dashboards in Power BI, Tableau, or Looker. Work with actual datasets (Kaggle or open data).
• Get familiar with ETL / data pipelines — even if you will not construct them completely, get familiar with tools or frameworks (Airflow, AWS Glue, Azure Data Factory, etc.).
• Learn Python / R fundamentals — for analytical scripting, data transformation, statistical functions.
• Work with public datasets / competitions — Kaggle, analytics hackathons to build a portfolio.
• Learn business & domain — choose a domain such as e-commerce, finance, supply chain, and learn its metrics, KPIs, and terminologies.

Project & Portfolio Work

• Capstone / end-to-end projects: From data collection → cleaning → modeling → dashboard → insights.
• Dashboard portfolios: Build 2–3 dashboards showing different types (sales, churn, operations) and host them on public sites or GitHub (with sample data).
• Contribute to open source / community analytics code or blogs.
• Internships & freelance work: Even short assignments in data/BI help.

Mock Interviews & Case Studies

• Case study practice: E.g. “Here’s data of sales and marketing spend by region — show how you’d analyze ROI, trends, recommendations.”
• Technical test practice: SQL challenges, data cleaning tasks, debugging, complex joins, window functions.
• Dashboard walkthrough: Given a dashboard mockup, explain how you’d build it — what filters, interactivity, chart types.
• Behavioral & communication practice: Explain a technical insight to a non-technical stakeholder; defend data decisions.
• Data reasoning: Given data anomalies or trends, articulate hypotheses and suggest tests / validation.

Resume & Profile Strengthening

• Highlight associated projects & tools: Place keywords such as “SQL, dashboard, ETL, Tableau, Power BI.”
• Business impact description: Even in a student project, say “improved insight clarity” or “cut report load time by 40%.”
• Clean, professional format: Highlight data, analytics, and problem-solving skills.
• LinkedIn / GitHub: Post dashboard projects, share analytics content, post/mini-blogs on insights.
• Certifications: BI tool certifications, data analytics, cloud (if applicable).

Sample Interview Questions & Tips

Here are some probable questions you might be asked and how to prepare:

SQL / Technical Questions

• “Write a query to get the top 3 customers by revenue last quarter.”
• “Compute cumulative sum of sales per month per region using window functions.”
• “Two tables orders and returns, how do you calculate net revenue per customer?”
• “How would you rewrite a slow query with lots of joins on large tables?”

Data Modeling & Design

• “Design a star schema for an e-commerce company (orders, products, customers, returns).”
• “What aggregate tables would you precompute?”
• “How would you design a dimensional model for usage of subscription-based services?”

Dashboard / BI Design

• “You need to construct a churn dashboard — what metrics and visuals will you include?”
• “How do you select chart types (bar, line, heatmap, scatter) for a dataset?”
• “How will you implement drill-downs, filtering, and interactivity?

Analytics Case / Business Reasoning

• “Revenue fell 10% last month — how will you follow up on this?”
• “How to segment customers to drive targeted marketing?”
• “With campaign spend and outcome data provided, how do you calculate ROI and what does it mean?”

Soft / Behavioral Questions

• “Tell me about a time when your data insight impacted a decision.”
• “How do you communicate complicated metrics to a non-technical stakeholder?”
• “How do you deal with two business stakeholders asking conflicting things?”
• “Tell me about a time you encountered data quality problems — how did you resolve them?”

Situational / Hypothetical

• “If there is a sudden increase in user drop-offs reported by the dashboard, what actions would you take to verify and diagnose?”
• “You have sparse data from specific regions. How do you offer rough estimates or express doubt?”
• Tips:
  Always define the problem first before leaping to the solution.
• Utilize structure in your responses: e.g. approach → steps → deliverables → pitfalls.
• Describe trade-offs (e.g. accuracy vs performance, real-time vs batch).
• Support answers with examples from your projects.
• In dashboard/design questions, verbalize your thinking (why bar vs line, filter setup, etc.).

Challenges You Might Encounter in This Role & How to Address

• Challenge 1: Data Quality & Missing / Inconsistent Data
• Solution: Develop robust validation rules, reconciliation checks, missing data alerting, and make data assumptions transparent.
• Challenge 2: Misalignment of Expectations
• Stakeholders may request unachievable things from data.
  Solution: Establish expectation early, suggest phased deliverables, use alternative metrics if key data is not present.
• Challenge 3: Scaling & Performance Issues
• Dashboards or queries will slow as volumes of data increase.
  Solution: Utilize aggregates, caching, optimized indexing, partitioning, precomputed tables, incremental loads.
• Challenge 4: Too Many Ad Hoc Requests
• Too many small requests will sidetrack roadmap.
  Solution: Prioritize requests, establish SLAs, negotiate scope, batch similar requests, have a backlog.
• Challenge 5: Changing Business Logic Quickly
• Business definitions or processes can change (e.g. product definitions, metric definitions).
  Solution: Use versioning, data lineage, solid documentation, and model flexibly.
• Challenge 6: Technical-Business Stakeholder Communication Gap
• Insights can get lost in translation.
  Solution: Leverage clear visuals, storytelling, contextual stories, train end-users on dashboards, engage stakeholders early.

Why this Role at NTT DATA is Appealing

• Global exposure & variety of projects: Being an international client base for NTT DATA, you could work across geographies, industries, and domains.
• Room for growth: Being a fresher is an eligibility criterion, so vertical growth and skill widening are possible.
• Strong brand and stability: NTT DATA is a well-established company with numerous ongoing analytics projects.
• Opportunity to learn cutting-edge tools: Exposure to state-of-the-art BI, cloud, and data platform tools.
• Influential role: By virtue of being a BI analyst, you have the ability to directly impact business decisions, revenue, and operations.