Senior Staff Engineer, Big Data Engineer

Nagarro; Chennai District; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Nagarro is seeking a Senior Data Engineer in India to drive data engineering initiatives across scalable data pipelines and architectures. The role demands deep expertise in Spark, Python/PySpark, data warehousing, and cloud-native deployments, with emphasis on data governance and secure data processing.

The candidate will collaborate with cross-functional teams to design, implement, and optimize ETL/ELT workflows, ensuring robust data quality and integration across banking/financial domains.

Qualifications

• 8+ years of experience in data engineering roles.
• Excellent knowledge and experience in big data engineering.
• Strong experience in Spark, Python/PySpark, data architecture and data warehousing.
• Experience with Hadoop, Hive, Presto/Tez and cloud services.

Responsibilities

• Write and review high-quality code for data processing.
• Translate client use cases into technical designs.
• Design and review architecture for scalability, security, and non-functional requirements.
• Develop ETL/ELT workflows and data ingestion pipelines.
• Collaborate with DevOps to automate deployments and CI/CD pipelines.
• Define guidelines and benchmarks for NFR considerations in projects.
• Document architecture and high-level design for the team.

Skills

Spark

Python/PySpark

Data Architecture

Data Warehousing

Hadoop

SQL

Cloud (AWS/Azure)

CI/CD

Data Governance

Banking/Financial Domain

Tools

Git

Jenkins

CI/CD

Job description

We're Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (17500 experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

REQUIREMENTS:

• Total experience: 8+ years.
• Excellent knowledge and experience in Bigdata engineer role.
• Strong working experience with architecture and development in Spark, Python/Pyspark, Data Architecture, Data Warehousing, Banking/Financial Domain, Hadoop, CICD, SQL.
• Data Engineering & Data Architecture
• Data Warehousing, Data Vault, Star/Snowflake Modelling
• Hadoop, Spark, Hive, Presto, Tez
• Python, PySpark, Linux
• Git, Jenkins, CI/CD
• Cloud-native architecture and deployments
• Data Governance, Data Quality, Metadata Management
• Banking/Financial Services domain experience
• Good understanding of AWS/Azure cloud services.
• Collaborate with DevOps teams to automate deployments and CI/CD pipelines.
• Write efficient, reusable, and scalable code for data processing and automation.
• Implement advanced data transformations, validations, and quality checks and hands-on experience in data ingestion pipelines.
• Develop ETL/ELT workflows to ingest, transform, and process structured and unstructured data.
• Good communication and interpersonal skills.

RESPONSIBILITIES:

• Writing and reviewing great quality code.
• Understanding the clients business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
• Mapping decisions with requirements and be able to translate the same to developers.
• Identifying different solutions and being able to narrow down the best option that meets the clients requirements.
• Defining guidelines and benchmarks for NFR considerations during project implementation.
• Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers.
• Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
• Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it.
• Understanding and relating technology integration scenarios and applying these learnings in projects.