NTT DATA is Hiring: Data Engineer | Finsplitz

Introduction

Are you passionate about building robust data pipelines and transforming raw data into valuable assets? NTT DATA, a global IT services provider and a leader in digital business and IT transformation, is actively seeking talented Data Engineers across its major development centers in India, including Bengaluru, Chennai, Hyderabad, and Pune. NTT DATA partners with a vast array of clients across diverse industries such as banking, healthcare, automotive, and public sector, helping them harness the power of data through advanced analytics, AI, and cloud solutions. As a Data Engineer at NTT DATA, you will be instrumental in designing, constructing, and maintaining scalable and efficient data architectures that enable critical business intelligence and data science initiatives. This role offers an excellent opportunity to work with cutting-edge data technologies, solve complex data challenges for global clients, and accelerate your career in the rapidly evolving data domain.

Roles and Responsibilities

A Data Engineer at NTT DATA plays a pivotal role in ensuring that data is accessible, reliable, and optimized for analytical and operational use. They are involved in the entire data lifecycle, from ingestion to transformation and delivery.

Typical responsibilities for a Data Engineer at NTT DATA include:

• Data Pipeline Design & Development (ETL/ELT):
  • Designing, building, and maintaining robust and scalable data pipelines to extract, transform, and load (ETL/ELT) data from various source systems (on-premise databases, cloud applications, APIs, streaming sources) into data warehouses, data lakes, or other data stores.
  • Utilizing tools and technologies such as SQL, Python, Spark, Kafka, Informatica, Talend, Azure Data Factory, AWS Glue, Google Cloud Dataflow for data integration.
  • Ensuring data lineage, data governance, and data security within the pipelines.
• Data Modeling & Architecture:
  • Designing and implementing optimal data models (e.g., star schema, snowflake schema, 3NF) for analytical purposes, ensuring efficient data storage and retrieval.
  • Contributing to the overall data architecture strategy, including data lake design, data warehousing solutions, and real-time data processing frameworks.
  • Working with databases like PostgreSQL, Redshift, Snowflake, Databricks, Azure SQL, or Oracle.
• Data Quality & Optimization:
  • Implementing data quality checks, validation rules, and monitoring mechanisms to ensure accuracy, completeness, and consistency of data.
  • Troubleshooting data-related issues, performing root cause analysis, and implementing corrective actions.
  • Optimizing existing data pipelines and queries for performance and cost efficiency.
• Collaboration & Documentation:
  • Collaborating closely with data scientists, data analysts, business intelligence developers, and solution architects to understand their data requirements and deliver appropriate data solutions.
  • Creating detailed technical documentation for data pipelines, data models, and architectural designs.
  • Participating in Agile/Scrum ceremonies and contributing to sprint planning and retrospectives.
• Cloud Data Services:
  • Leveraging various cloud data services (e.g., AWS S3, Redshift, Glue; Azure Data Lake, Data Factory, Synapse; GCP BigQuery, Dataflow) to build cloud-native data solutions.

Data Engineers at NTT DATA are expected to be strong problem-solvers, detail-oriented, have excellent programming and SQL skills, and be adaptable to a wide range of technologies and client requirements.

Salary and Benefits

NTT DATA offers a competitive compensation package for Data Engineers in India, aligning with industry standards for large IT services and consulting firms. The compensation typically includes a fixed salary component, performance-linked incentives, and comprehensive benefits.

• Average Annual CTC (Cost to Company) in India:
  • Data Engineer (Entry-Level / 0-2 years experience): The typical annual CTC can range from ₹4 lakhs to ₹8 lakhs per annum. This generally includes base salary and standard allowances.
  • Data Engineer (2-4 years experience): The average annual CTC can range from ₹7 lakhs to ₹14 lakhs per annum.
  • Senior Data Engineer (4-8 years experience): The average annual CTC can range from ₹12 lakhs to ₹20 lakhs+ per annum, varying based on specialized skills (e.g., cloud data engineering) and leadership responsibilities.
  • Lead Data Engineer / Data Architect (8+ years experience): Compensation can go significantly higher, ranging from ₹18 lakhs to ₹30 lakhs+ per annum, depending on technical depth, team leadership, and scope of impact.
  • Note: These figures are indicative and can vary based on factors such as educational background, specific technology expertise (e.g., advanced cloud skills, big data platforms), location (Bengaluru/Hyderabad might offer slightly higher averages), individual performance, and the specific client project and domain.
• Comprehensive Benefits and Perks: NTT DATA provides a robust set of benefits aimed at supporting employee well-being, professional growth, and work-life integration.
  • Health & Wellness: Comprehensive medical insurance coverage for employees and their families, life insurance, and accidental insurance. Access to wellness programs and employee assistance programs.
  • Financial Benefits: Provident Fund (PF), Gratuity, and performance-based bonuses linked to individual and company performance.
  • Paid Time Off: Generous leave policies including privilege leave, casual leave, sick leave, and public holidays.
  • Learning & Development: Significant investment in continuous learning and professional development. Access to extensive internal training platforms, industry certifications (e.g., AWS Certified Data Engineer, Azure Data Engineer Associate, Databricks certifications), and opportunities for upskilling in emerging data technologies like GenAI and advanced analytics.
  • Career Progression: Defined career paths within the data and analytics ladder (from Data Engineer to Senior, Lead, and Architect roles). Opportunities for cross-skilling and exposure to diverse client projects and domains.
  • Global Exposure: Opportunity to work on global projects and collaborate with diverse teams across different geographies and cultures.
  • Work-Life Balance: NTT DATA generally promotes a healthy work-life balance and may offer flexible working arrangements or a hybrid work model depending on client and project needs.
  • Employee Engagement: Various employee networks, clubs, and social initiatives fostering an inclusive and engaging work culture.

Eligibility Criteria

NTT DATA seeks Data Engineers who are technically proficient, eager to solve complex data challenges, and adaptable to various industry requirements.

• Educational Qualification:
  • Bachelor’s or Master’s degree (B.E./B.Tech/MCA/M.Tech) in Computer Science, Information Technology, Software Engineering, Data Science, or a related quantitative field from a recognized university.
  • A strong academic record is typically preferred.
• Experience:
  • For Freshers/Entry-Level (0-1 year): Recent graduates with a strong academic foundation in data structures, algorithms, and databases. Relevant coursework, significant academic projects, or internships in data engineering/analytics are highly valued.
  • For Experienced Roles (1+ years): Minimum of 1-3 years (for mid-level) to 4-8+ years (for senior/lead roles) of professional experience in data engineering, ETL development, or big data engineering roles.
• Key Technical Skills (Essential):
  • Programming Languages: Strong proficiency in Python (most common for scripting, data processing) or Java/Scala (for big data frameworks like Spark).
  • SQL Proficiency: Expert-level ability to write complex SQL queries for data extraction, manipulation, transformation, and optimization for various databases.
  • ETL/ELT Tools & Concepts: Strong understanding of ETL/ELT methodologies and hands-on experience with ETL tools (e.g., Informatica, Talend, Azure Data Factory, AWS Glue) or custom scripting.
  • Data Warehousing/Data Lake: Understanding of data warehousing concepts, dimensional modeling (star schema, snowflake schema), and experience with data lake concepts.
  • Version Control: Proficiency with Git.
• Key Technical Skills (Highly Desirable/Good to Have):
  • Big Data Technologies: Experience with Spark (PySpark/Scala Spark), Hadoop, Kafka, or other distributed processing frameworks.
  • Cloud Platforms: Hands-on experience with data services on at least one major cloud platform (AWS, Azure, or GCP). Cloud certifications are a significant advantage.
  • NoSQL Databases: Familiarity with NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB).
  • Orchestration Tools: Experience with orchestrators like Airflow, AWS Step Functions, Azure Data Factory.
  • CI/CD: Understanding of CI/CD pipelines for data engineering workflows.
  • Data Governance & Security: Awareness of data governance, data quality, and security best practices in data pipelines.
  • Domain Knowledge: Prior exposure to specific industry domains (e.g., Financial Services, Telecommunications, Healthcare) is a plus.

Application Process

The hiring process for Data Engineer roles at NTT DATA is designed to assess a candidate’s technical depth, problem-solving skills, and cultural fit within a client-centric IT services organization.

1. Online Application:
   • Candidates typically apply through NTT DATA’s official careers portal or major job platforms.
   • Submit a detailed Resume/CV highlighting your educational qualifications, programming skills, experience with data technologies (SQL, Python, ETL tools, cloud platforms), and relevant projects or internships.
2. Resume Screening:
   • HR and the hiring team review applications to shortlist candidates whose profiles align best with the role’s requirements.
3. Online Assessment (Potential):
   • For some roles, especially entry-level or mass hiring drives, an online assessment might be conducted. This can include:
     • Coding Challenges: Problems focusing on Data Structures and Algorithms, typically in Python or Java.
     • SQL Queries: Practical questions to test SQL proficiency (joins, aggregations, subqueries).
     • Technical MCQs: Questions on data warehousing concepts, ETL, databases, and relevant programming languages.
     • Aptitude/Logical Reasoning: General analytical and problem-solving questions.
4. Technical Interview Rounds (1-3 rounds, virtual or in-person):
   • These rounds focus on assessing your technical skills and practical experience.
   • Coding/DSA: You might be asked to solve coding problems related to data manipulation, algorithm design, or optimizing data processing logic.
   • Data Engineering Concepts: In-depth questions on data warehousing, ETL/ELT processes, data modeling, database design, and optimization techniques.
   • SQL & Python/Java: Practical coding questions in SQL and your chosen programming language for data-related tasks.
   • Big Data & Cloud (for relevant roles): Discussions on Spark, Kafka, Hadoop, and hands-on experience with AWS, Azure, or GCP data services.
   • Project Discussion: Detailed discussion of your past data engineering projects – your role, the architecture, challenges faced, and how you ensured data quality and scalability.
5. Managerial/Behavioral Round:
   • This round assesses your soft skills, communication, teamwork abilities, problem-solving approach to non-technical scenarios, and cultural fit.
   • Questions: “Tell me about a challenging data project,” “How do you handle data quality issues?”, “Why NTT DATA?”, “How do you collaborate with different teams?”
6. HR Round:
   • The final discussion typically covers compensation, benefits, and formal offer details.

Preparation Tips:

• Master SQL and Python/Java: These are foundational. Practice complex SQL queries and advanced data manipulation using Pandas in Python or equivalent in Java.
• Understand Data Warehousing & ETL: Be very clear on dimensional modeling, facts, dimensions, and the ETL/ELT process.
• Cloud Data Services: If applying for cloud-focused roles, get hands-on experience with services on AWS, Azure, or GCP relevant to data engineering (e.g., S3, Glue, Redshift; Data Lake, Data Factory, Synapse; BigQuery, Dataflow).
• Big Data Frameworks: For roles requiring it, deep dive into Spark (PySpark/Scala Spark) and Kafka. Understand their architecture and use cases.
• Practice Problem Solving: Be ready to articulate your approach to designing data pipelines, handling data quality issues, and optimizing performance.
• Communication Skills: Practice explaining technical concepts clearly and concisely to both technical and non-technical audiences.
• Research NTT DATA: Understand their service offerings, key clients, and recent data & analytics initiatives.

Conclusion

A career as a Data Engineer at NTT DATA in India offers an exciting pathway to contribute to large-scale data transformation initiatives for diverse global clients. You will gain exposure to a wide array of cutting-edge data technologies, continuously enhance your technical skills, and play a crucial role in enabling data-driven decision-making. If you are a technically strong, problem-solving individual with a passion for building robust data ecosystems, NTT DATA provides a solid platform for a growth-oriented career in the data domain.