Motorola is Hiring: Data Engineer | Finsplitz

Introduction

Are you passionate about transforming raw data into actionable insights and building robust data pipelines that power intelligent solutions? Motorola, a globally recognized brand with distinct entities like Motorola Solutions (mission-critical communications and public safety) and Motorola Mobility (consumer smartphones and mobile technology), is actively seeking talented Data Engineers for its innovation and development centers in India, primarily in Bengaluru. Data is at the core of Motorola’s strategic initiatives, enabling advanced analytics, AI-driven features, and optimizing operations across its diverse product lines, from public safety platforms to consumer devices. As a Data Engineer at Motorola, you will be instrumental in designing, building, and maintaining the scalable data infrastructure that supports critical business functions, enhances product performance, and contributes to creating a safer and more connected world. This role offers an exciting opportunity to work with large-scale datasets, cutting-edge big data technologies, and contribute to products that impact millions globally.

 

Roles and Responsibilities

 

A Data Engineer at Motorola is responsible for the entire lifecycle of data, from ingestion and transformation to storage and accessibility. Their role is crucial in creating reliable and optimized data pipelines that serve data scientists, analysts, and various business stakeholders. The specific responsibilities can vary depending on whether the role is within Motorola Solutions (focus on public safety, enterprise software, video security) or Motorola Mobility (focus on smartphones, consumer electronics).

Typical responsibilities for a Data Engineer at Motorola include:

• ETL/ELT Pipeline Development & Maintenance:
  • Designing, developing, and maintaining scalable and efficient ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) pipelines to ingest data from various sources (databases, APIs, streaming data, cloud storage).
  • Using programming languages like Python (with libraries like NumPy, Pandas, PySpark) and orchestration tools like Apache Airflow to automate data workflows.
  • Optimizing existing pipelines for performance, scalability, and cost-efficiency.
• Big Data Platform Management:
  • Working with large-scale data processing technologies and frameworks such as Apache Spark, Hadoop, Kafka, and distributed file systems.
  • Utilizing cloud-based big data services on platforms like AWS, GCP (Google Cloud Platform – e.g., BigQuery), or Azure.
• Data Modeling & Database Design:
  • Designing and implementing data models for data warehouses, data lakes, and analytical databases to ensure data integrity, consistency, and optimal query performance.
  • Proficiency in working with SQL and various database systems (relational and NoSQL).
• Data Quality & Governance:
  • Implementing data quality checks, monitoring data pipelines, and troubleshooting data-related issues to ensure accuracy and reliability of data.
  • Collaborating with data governance teams to establish data standards and ensure compliance.
• Collaboration & Requirements Gathering:
  • Working closely with data scientists, data analysts, software engineers, and business stakeholders to understand their data needs and translate them into technical solutions.
  • Ensuring efficient data flow and addressing critical data requirements for reporting, analytics, and machine learning models.
• Automation & Optimization:
  • Identifying opportunities for process automation and leveraging tools and scripting to improve data engineering workflows.
  • Applying server-side development skills like multithreading and asynchronous I/O where applicable.
• Documentation:
  • Creating and maintaining clear technical documentation for data pipelines, data models, and processes.

Data Engineers at Motorola are expected to be strong problem-solvers, detail-oriented, have excellent analytical skills, and be able to work independently as well as collaboratively in a fast-paced environment.

 

Salary and Benefits

 

Motorola offers a competitive salary and comprehensive benefits package for Data Engineers in India, aligning with industry standards for product and technology-focused companies. The compensation structure typically includes a base salary, performance-based bonuses, and a range of employee benefits.

• Average Annual CTC (Cost to Company) in India:
  • Data Engineer (Entry-Level / 0-2 years experience): The typical annual CTC for an entry-level or early career Data Engineer can range from ₹8 lakhs to ₹16 lakhs per annum. For freshers with no prior experience, reported salaries can start around ₹10 lakhs to ₹12 lakhs.
  • Data Engineer (2-5 years experience): The average annual CTC can range from ₹15 lakhs to ₹25 lakhs per annum.
  • Senior Data Engineer (5+ years experience): For more senior roles, the annual CTC can range from ₹20 lakhs to ₹40 lakhs+ per annum, depending on specialized expertise (e.g., advanced cloud data engineering, big data architecture), leadership responsibilities, and overall impact.
  • Note: These figures are indicative and can vary based on factors such as educational background, specific technology stack proficiency, interview performance, and the business unit (Motorola Solutions often has higher compensation bands due to the complexity of its enterprise and public safety solutions).
• Comprehensive Benefits and Perks: Motorola provides a robust set of benefits designed to support employee well-being, professional growth, and work-life balance.
  • Health & Wellness: Comprehensive medical insurance coverage for employees and their families, life insurance, accidental insurance, and access to various wellness programs.
  • Financial Security: Provident Fund (PF), Gratuity, and performance-based variable pay or bonuses tied to individual and company performance.
  • Paid Time Off: Generous leave policies including annual leave, sick leave, casual leave, and company holidays. Parental leave benefits are also provided.
  • Learning & Development: Significant investment in continuous learning and career development. Access to internal learning platforms, external courses, and opportunities for industry-recognized certifications (e.g., cloud certifications from AWS, GCP, Azure; Apache Spark certifications). Motorola emphasizes upskilling in cutting-edge technologies.
  • Career Progression: Clear career paths within the data and analytics domains, allowing progression from Data Engineer to Senior Data Engineer, Lead Data Engineer, Data Architect, or into management roles. Opportunities for specialization in areas like AI Data Engineering or Cloud Data Engineering.
  • Global Exposure: Opportunity to work on global projects and collaborate with diverse teams across different geographies, especially within Motorola Solutions’ worldwide operations.
  • Work-Life Balance: Motorola generally promotes a healthy work-life balance, and depending on the role and team, there might be flexibility in working arrangements.
  • Employee Engagement: Various employee networks, clubs, and social initiatives fostering an inclusive and engaging work culture.

 

Eligibility Criteria

 

Motorola looks for Data Engineers who have strong technical foundations, hands-on experience with data technologies, and a passion for building scalable and reliable data solutions.

• Educational Qualification:
  • Bachelor’s or Master’s degree (B.E./B.Tech/MCA/M.Tech/MS) in Computer Science, Information Technology, Software Engineering, Data Science, or a closely related quantitative or technical discipline from a recognized university.
  • A strong academic record is generally preferred.
• Experience:
  • For Freshers / Entry-Level (0-2 years experience): Recent graduates with a strong academic background in data structures, algorithms, and database concepts. Relevant academic projects, internships focused on data engineering, or contributions to open-source projects demonstrating practical skills in Python, SQL, and basic cloud concepts are highly valued.
  • For Experienced Professionals (2+ years experience): Minimum of 2+ years of professional experience in a Data Engineer, ETL Developer, Big Data Engineer, or similar role.
• Key Technical Skills (Essential):
  • Programming Language: Strong proficiency in Python for data manipulation, scripting, and ETL development.
  • SQL: Expert-level proficiency in SQL for querying, manipulating, and analyzing data in relational databases. Experience with BigQuery (GCP) or similar cloud data warehouses is a strong plus.
  • Big Data Frameworks: Hands-on experience with Apache Spark (especially PySpark). Knowledge of Hadoop, Kafka is also highly desirable.
  • ETL/Workflow Orchestration: Experience with Apache Airflow for building and managing data pipelines.
  • Cloud Platforms: Proficiency in at least one major cloud platform for data engineering solutions (AWS, GCP, or Azure). This includes services like S3/ADLS, Redshift/BigQuery/Synapse, EMR/Databricks, etc.
  • Data Structures & Algorithms: Solid understanding of these fundamentals, as they apply to efficient data processing.
  • Version Control: Familiarity with Git for code management.
• Key Technical Skills (Highly Desirable/Good to Have):
  • Experience with other data warehousing solutions or NoSQL databases (e.g., Cassandra, MongoDB).
  • Knowledge of data visualization tools like Power BI or Tableau.
  • Familiarity with server-side development concepts like multithreading and asynchronous I/O.
  • Experience with data quality tools and methodologies.
  • Understanding of CI/CD pipelines for data solutions (e.g., using Azure DevOps, GitHub Actions).
  • Exposure to machine learning concepts and how data pipelines support ML models.
• Key Soft Skills:
  • Strong Analytical & Problem-Solving Skills: Ability to analyze complex data problems, identify root causes, and design effective solutions.
  • Excellent Communication: Strong verbal and written communication skills to collaborate effectively with technical and non-technical stakeholders.
  • Teamwork & Collaboration: Ability to work effectively in an agile, cross-functional team environment.
  • Attention to Detail: Meticulousness in ensuring data accuracy and pipeline reliability.
  • Learning Agility: Eagerness to learn new technologies, adapt to evolving data ecosystems, and solve challenging problems.

 

Application Process

 

The hiring process for Data Engineer roles at Motorola is typically comprehensive, designed to evaluate a candidate’s technical expertise, problem-solving capabilities, and cultural fit within a fast-paced technology environment.

1. Online Application:
   • Candidates apply through Motorola’s official careers portal (e.g., Motorola Solutions Careers or Lenovo Careers for Motorola Mobility roles).
   • Submit a detailed Resume/CV highlighting your educational qualifications, relevant data engineering projects, specific technical skills (Python, SQL, Spark, Cloud platforms), and any internships or professional experience.
2. Resume Screening:
   • Recruiters and hiring managers review applications to shortlist candidates whose profiles best align with the job requirements.
3. Online Assessment (Potential):
   • For some roles, particularly entry-level positions, an online assessment may be conducted. This often includes:
     • Coding Challenges: 1-2 problems testing your proficiency in Python and your understanding of data structures and algorithms.
     • SQL Assessment: Questions testing your SQL querying skills (joins, aggregations, subqueries, window functions).
     • Technical MCQs: Questions on data warehousing concepts, big data technologies, cloud fundamentals, and basic data engineering principles.
     • Aptitude Test: May include logical reasoning and quantitative aptitude.
4. Technical Interview Rounds (2-3 rounds, virtual or in-person):
   • Conducted by senior Data Engineers or Leads.
   • Focus: In-depth assessment of your technical knowledge and practical skills.
   • Common topics include:
     • Python: Advanced Python concepts, problem-solving using Python, design patterns, scripting.
     • SQL: Complex SQL queries, database design, normalization, indexing, performance tuning.
     • Big Data Technologies: Deep dive into Spark architecture, RDDs/DataFrames, Spark optimizations, Hadoop ecosystem components, Kafka for streaming.
     • Cloud Data Services: Detailed discussion on cloud-native data services you’ve used (e.g., AWS Glue, S3, Redshift; GCP Dataflow, BigQuery, Cloud Storage; Azure Data Factory, Data Lake, Synapse).
     • Data Modeling: Discussion on dimensional modeling (Star, Snowflake schemas), normalized vs. denormalized models.
     • ETL/Data Pipeline Concepts: Designing end-to-end data pipelines, error handling, monitoring, scheduling (Airflow concepts).
     • Project Discussion: Detailed discussion of your past data engineering projects, focusing on your role, technical challenges, design decisions, and impact.
5. Managerial/Behavioral Interview:
   • This round assesses your soft skills, problem-solving approach to real-world scenarios, teamwork abilities, and cultural fit with Motorola’s values.
   • Questions: Behavioral questions using the STAR method (Situation, Task, Action, Result), such as “Tell me about a time you dealt with a data quality issue,” “How do you handle disagreements with team members?”, or “Describe a challenging data engineering problem you solved.”
6. HR Interview:
   • Final discussion regarding compensation, benefits, and overall fit with the company.
7. Offer & Background Check:
   • If successful, a formal offer is extended, followed by a background verification process.

Preparation Tips:

• Master Python & SQL: These are foundational. Practice coding problems involving data manipulation, algorithms, and complex SQL queries (especially window functions and CTEs).
• Deep Dive into Spark: Understand Spark’s architecture, transformations, actions, and how to optimize Spark jobs.
• Learn a Cloud Platform: Choose one cloud platform (AWS, GCP, or Azure) and gain hands-on experience with its key data services. Obtain a relevant certification if possible.
• Practice ETL Design: Be ready to design data pipelines from scratch for various scenarios, discussing challenges like data quality, scalability, and error handling.
• Review Data Warehousing Concepts: Understand dimensional modeling and data lake architectures.
• Hone Communication Skills: Practice articulating your technical ideas clearly and concisely.
• Research Motorola: Understand Motorola’s different business segments (Solutions vs. Mobility), their mission, and how data plays a role in their products and services. Be prepared to discuss how your skills align with their goals of connecting people and public safety.

 

Conclusion

 

A career as a Data Engineer at Motorola in India offers an intellectually stimulating and impactful journey for professionals passionate about building robust data ecosystems. You will contribute to a global brand that is shaping the future of communication and public safety, working with cutting-edge data technologies and solving complex, real-world problems. If you are an analytical, detail-oriented individual with strong programming skills and a passion for data, Motorola provides an excellent platform for a rewarding and influential career in data engineering.