KASHII UPDATEZ Everyday Student Requirements & Python Coding Tutorials by Python Kashi
REQUIREMENT_ID_1394 β€’ 3-DAY_ACTIVE_POLICY

Data Analytics Engineer

Company
Company Aptur
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 12-18 LPA
Location
Location Bengaluru, Karnataka
Posted Date
Posted Date Today
SQL Python Data Modeling ETL Data Warehousing Power BI Tableau Cloud Platforms Problem Solving Communication
πŸ“–
Aptitude Practice Questions Open Resource β†—
Covers quantitative and logical reasoning problems that help sharpen the analytical mindset required for Aptiv's assessment.
πŸ“–
Data Analytics Interview Preparation Open Resource β†—
Provides curated interview questions and concepts specific to data analytics engineering roles, useful for Aptiv's technical rounds.
πŸ“–
Comprehensive Programming Guide Open Resource β†—
Offers tutorials and practice exercises on Python and SQL, essential for building data pipelines and solving coding assessments.
πŸ“–
Algorithm Problem Set Open Resource β†—
A collection of coding problems to improve problem‑solving speed and accuracy, beneficial for Aptiv's online test.

Graduates (B.Tech/B.E/M.Tech/M.Sc) in Computer Science, Information Technology, Electronics & Communication, Statistics, Mathematics or related fields. Minimum 60% aggregate (or 6.0 CGPA). Final year students of 2025 batch eligible. Maximum of 2 active backlogs allowed at the time of application. Must have strong foundation in SQL, Python and data analytics concepts.

1
Round 1: Online Assessment (SQL & Python coding)
2
Round 2: Technical Interview (data pipeline design, case studies)
3
Round 3: HR Interview (fitment and behavioral questions)
Aptiv is a global technology company that designs and manufactures innovative solutions for the automotive and mobility sectors. With a presence in over 40 countries, the firm focuses on creating safer, greener, and more connected experiences for drivers and passengers. In India, Aptiv has established a strong engineering hub in Bengaluru, where it collaborates with cross‑functional teams to deliver cutting‑edge software, hardware, and data‑driven products. The company’s culture encourages continuous learning, diversity of thought, and a passion for solving complex real‑world problems. The Data Analytics Engineer role at Aptiv Bengaluru is a pivotal position that bridges the gap between raw data and actionable insights. As a member of the analytics engineering team, you will design, build, and maintain robust data pipelines that ingest, cleanse, and transform large volumes of structured and unstructured data. You will work closely with data scientists, product managers, and business stakeholders to develop dashboards, reports, and predictive models that drive strategic decisions across the organization. Key responsibilities include: 1. Designing end‑to‑end ETL pipelines using SQL, Python, and modern data orchestration tools. 2. Developing and optimizing data models for data warehousing solutions such as Snowflake or Redshift. 3. Implementing data quality checks and monitoring frameworks to ensure reliability. 4. Creating interactive visualizations and self‑service dashboards in Power BI or Tableau. 5. Collaborating with cross‑functional teams to translate business requirements into technical specifications. 6. Conducting performance tuning of queries and pipelines to meet SLA targets. 7. Documenting data lineage, architecture, and standard operating procedures. 8. Staying updated with emerging data technologies and recommending best practices. 9. Supporting ad‑hoc analytical queries and troubleshooting data issues. 10. Mentoring junior engineers and contributing to knowledge‑sharing sessions. The tech stack typically includes SQL, Python, Airflow, dbt, cloud platforms (AWS/GCP), and BI tools like Power BI/Tableau. Aptiv offers a clear growth path: junior engineers can progress to senior engineer, lead data architect, or analytics manager roles, with opportunities to work on global projects and gain exposure to the automotive IoT ecosystem. Joining Aptiv means being part of a forward‑thinking organization that values innovation, offers competitive compensation, and provides a collaborative environment where your contributions directly impact the future of mobility.

Aptur β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
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 🎯
Aptur Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Aptur and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
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 πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
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 (SQL, Python, Data Modeling, ETL, Data Warehousing, Power BI, Tableau, Cloud Platforms, Problem Solving, Communication) & 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
Aptur Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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.
Why do you want to join Aptur as a Data Analytics Engineer?
Preparation Tip: Highlight Aptur's market reputation, recent tech innovations, and how your skills in SQL 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.

More Fresh Requirements in Software Engineering

View Category Feed β†—
Aptur
Data Analytics Engineer
Apply Apply Now β†—
Chat Chat with Kashii