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Required Skills & Tech Stack
Power BI
SQL
SAP
Power Platform
Data Analytics
Data Visualization
Data Governance
Excel
Python
Communication
Problem Solving
π Free Study Materials (4)
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Aptitude and Logical Reasoning Practice
Open Resource β
Helps sharpen analytical thinking and problemβsolving skills essential for data analysis roles.
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Technical Interview Preparation
Open Resource β
Provides practice questions and interview strategies for technical roles like data analyst and engineer.
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Career Development Resources
Open Resource β
Offers guidance on skill building, certifications, and career progression in data and analytics.
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Coding Challenges and Algorithms
Open Resource β
Enhances coding proficiency and algorithmic thinking useful for data manipulation and automation tasks.
Eligibility Criteria
Bachelor of Engineering (B.E.) or Bachelor of Technology (B.Tech.) in Mechanical, Electrical, Computer Science, Information Technology, or Data Science. Minimum 60% marks in the qualifying exam. 0-1 years of relevant experience. No backlog policy; candidates with backlogs may be considered on a caseβbyβcase basis.
Selection & Interview Process
1
Round 1: Technical β Data Analytics & Power BI
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Round 2: Technical β SQL & SAP
3
Round 3: HR
Job Description & Key Responsibilities
Hitachi Energy, a global leader in power and energy solutions, is a part of the larger Hitachi Group. With a strong focus on sustainability and digital transformation, the company delivers innovative products and services that help utilities and industrial customers reduce carbon emissions and improve operational efficiency. The Global Engineering Center in Vadodara serves as a hub for cuttingβedge research, development, and engineering excellence, supporting a diverse workforce that spans multiple disciplines.
The Data Analyst β Power BI, SQL, SAP role is a key position within the Digital Engineering and Design team. The successful candidate will be responsible for creating business reports, dashboards, and scorecards that provide actionable insights to engineering and leadership teams. By collecting, cleansing, and validating data from multiple enterprise systems, the analyst will enable dataβdriven decision making across the organization.
Key Responsibilities:
1. Collect, cleanse, validate, and analyze data from SAP, SQL databases, and other enterprise sources.
2. Develop interactive dashboards and reports using Power BI and related analytics tools.
3. Identify trends, performance gaps, risks, and improvement opportunities.
4. Establish automated reporting solutions to improve efficiency and accuracy.
5. Analyze engineering utilization, productivity, capacity, and workload data.
6. Support workforce planning, hiring forecasts, and resource optimization.
7. Develop KPI dashboards for engineering functions, managers, and leadership teams.
8. Monitor project delivery performance and engineering execution metrics.
9. Support implementation of digital tools and analytics platforms.
10. Develop automated workflows using Power Platform (Power Apps, Power Automate).
Tech Stack: Power BI, SQL, SAP, Power Platform (Power Apps, Power Automate), Excel, Python (optional), Data Governance tools.
Growth Path: Starting as a Data Analyst, you can progress to Senior Analyst, Analytics Lead, or Data Engineer roles. Opportunities to specialize in AI/ML, advanced analytics, or become a subject matter expert in Power Platform solutions are available.
Why Join Hitachi Energy? The company offers a collaborative environment that encourages innovation, provides exposure to global projects, and supports continuous learning. With a strong emphasis on sustainability, you will contribute to meaningful solutions that impact the future of energy.
The role is ideal for recent graduates who are passionate about data, eager to learn new technologies, and want to work in a dynamic, international setting.
Preparation Hub
Hitachi β Data Analytics & SQL Interview Guide
Previously asked questions, exam syllabus, coding benchmarks & round strategy.
π Previously Asked Questions & Preparation Links
APTITUDE & LOGIC
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Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
Open Resource β
COMPANY GUIDE
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Hitachi Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Hitachi and off-campus tech roles.
Open Resource β
MOCK TESTS & PAPERS
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Comprehensive Interview Prep Resources & Syllabus
Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.
Open Resource β
CODING PRACTICE
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Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
Open Resource β
π» Previously Asked Coding Challenges
Easy / Medium
β‘ Live Debugger
Two Sum & Two Pointers Array Optimization
Asked in 85% tech rounds
Medium
β‘ Live Debugger
Reverse Linked List & Detect Cycle (Floyd's Algorithm)
Frequently asked in core rounds
Medium
β‘ Live Debugger
SQL Nth Highest Salary with DENSE_RANK() & Subqueries
Top standard database question
Easy / Medium
β‘ Live Debugger
Valid Parentheses & Stack Implementation
Standard online assessment problem
πΊοΈ Round-Wise Strategy
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Power BI, SQL, SAP, Power Platform, Data Analytics, Data Visualization, Data Governance, Excel, Python, Communication, Problem Solving) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Hitachi Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
β Core Technical Questions & Answers
What is the difference between WHERE and HAVING clauses in SQL? Answer βΌ
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer βΌ
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer βΌ
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer βΌ
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer βΌ
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
π€ Behavioral & HR Questions (STAR Method)
Why do you want to join Hitachi as a Data Analyst β Power BI, SQL, SAP?
Preparation Tip: Highlight Hitachi's market reputation, recent tech innovations, and how your skills in Power BI directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.