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N #2927

Applications open Full-Time Job

Senior Data Engineer

Siemens

Pune, India
Competitive Package (Best in Industry)
4-7 Yrs
Posted Yesterday

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Recruiter contact

Recruiter / hiring contact

No public email in this post. Use a source-linked contact instead.

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About the role

You'll make a difference by: Providing technical support to teams adopting analytics platforms such as Adobe Analytics, Hotjar, and Moesif. Validating analytics implementations and troubleshooting tracking, integration, and data-quality issues. Integrating and processing analytics data through REST APIs.

Developing complex SQL queries and working with Snowflake data platforms. Designing, developing, and maintaining Power BI dashboards, reports, and semantic models. Translating product measurement requirements into meaningful metrics, data models, and reporting solutions.

Integrating data from multiple analytics and product sources to generate actionable business insights. Monitoring and improving data quality, reliability, and consistency across reporting systems. Collaborating with cross-functional teams to enable data-driven product and business decisions.

Contributing to scalable analytics and reporting solutions aligned with Building X objectives. You'll win us over by: Holding a Bachelor's or Master's degree in Computer Science, Information Technology, Electronics, Data Science, or a related engineering discipline. Bringing strong experience in data analytics, reporting, and software development.

Demonstrating hands-on expertise in SQL and Snowflake. Possessing strong knowledge of Power BI, data modeling, and data visualization best practices. Having experience integrating and supporting usage-tracking and analytics solutions such as Adobe Analytics, Hotjar, Moesif, or similar platforms.

Being proficient in JavaScript and/or TypeScript development. Having practical experience working with REST APIs and data integrations. Understanding analytics instrumentation, event tracking, and product measurement frameworks.

Being experienced with Git-based development workflows and CI/CD practices. Demonstrating strong troubleshooting, problem-solving, and software engineering capabilities. Nice to Have Experience with the Google HEART framework for product analytics.

Python programming skills. Experience in Cloud and SaaS-based application development. Knowledge of API monitoring, observability, and operational analytics.

Exposure to modern data engineering and analytics best practices.

Eligibility & process

Who can apply

Experience: 4-7 Yrs. Open to matching qualifications.

Skills & stack

Snowflake SQL Data Scripting Data Engineering Snowflake Sql Python JavaScript

Selection process

Online Assessment / Technical Interview Rounds / HR Review

Prep kit

20 resources

Study material 4

📖 Aptitude Practice Questions & Mock Tests Curated logical, quantitative, and verbal reasoning problems for the initial online screening round. 🎯 Company-Specific Interview Preparation Corner Detailed interview experiences, exam formats, and previous test questions for Siemens and tech roles. 📝 Technical Placement Question Bank High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides. 💻 Algorithm, DSA & Live Code Debugger Practice Hands-on problem sets to improve coding speed and step-by-step memory debugging.

Company-specific guides 4

📖 APTITUDE & LOGIC Aptitude Practice Questions & Online Mock Tests Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test. 🎯 COMPANY GUIDE Siemens Interview Preparation Corner Comprehensive guide covering typical interview formats, common questions, and preparation tips for Siemens and off-campus tech roles. 📝 MOCK TESTS & PAPERS Comprehensive Interview Prep Resources & Syllabus Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds. 💻 CODING PRACTICE Algorithm and Data Structure Problem Set Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Likely coding questions 4

💻 Two Sum & Two Pointers Array Optimization Easy / Medium Asked in 85% tech rounds ⚡ Live debugger 💻 Reverse Linked List & Detect Cycle (Floyd's Algorithm) Medium Frequently asked in core rounds ⚡ Live debugger 💻 SQL Nth Highest Salary with DENSE_RANK() & Subqueries Medium Top standard database question ⚡ Live debugger 💻 Valid Parentheses & Stack Implementation Easy / Medium Standard online assessment problem ⚡ Live debugger

Round-wise strategy 4

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 (Snowflake, SQL, Data, Scripting, Data Engineering, Snowflake Sql, Python, JavaScript) & 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 Siemens Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.

Technical questions 5

What is the difference between WHERE and HAVING clauses in SQL? Answer

Model answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.

⚡ Practice in the code editor

Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer

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.

⚡ Practice in the code editor

How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer

Model answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.

⚡ Practice in the code editor

What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer

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.

⚡ Practice in the code editor

How do you calculate MoM (Month-over-Month) growth in SQL? Answer

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.

⚡ Practice in the code editor

Behavioural & HR questions 3

Why do you want to join Siemens as a Senior Data Engineer?
Highlight Siemens's market reputation, recent tech innovations, and how your skills in Snowflake directly solve their team's objectives.

Describe a challenging bug or academic project roadblock and how you resolved it.
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

About the role

Eligibility & process

Who can apply

Skills & stack

Selection process

Prep kit

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