Data Engineer - Spark

Chargebee

Chennai On-site Full-time 1–2 Years Posted Oct 02, 2026

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₹3 – ₹6 LPA

Offered Salary (LPA)

Days Left to Apply

About This Role

Chargebee is hiring a Data Engineer – Spark in Chennai, Tamil Nadu, for its Data Platform team, which develops and maintains scalable data systems supporting internal analytics, business intelligence, and customer-facing data capabilities. This full-time opportunity is designed for professionals with around 1–2 years of experience who want to work with modern data engineering technologies and distributed processing systems. The role involves building reliable data pipelines across multiple stages of the data platform, including data ingestion, processing, transformation, and data serving. Engineers will work with technologies such as Apache Spark, Spark SQL, Python, Java, Scala, SQL, AWS, and distributed data systems, gaining practical exposure to large-scale data workloads and production-grade engineering environments.

As a Data Engineer at Chargebee, you will contribute to the development and continuous improvement of data infrastructure that transforms large volumes of raw information into reliable, usable datasets. The role includes designing and maintaining ETL and ELT pipelines, developing distributed processing jobs with Apache Spark, working with streaming and event-driven systems such as Kafka, and creating efficient data models and transformations for analytics and product use cases. You will also be involved in troubleshooting production pipelines, investigating distributed-system issues, improving data quality, and ensuring that data is processed efficiently. The position provides an opportunity to understand how modern data platforms combine batch processing, streaming architectures, cloud infrastructure, databases, data lakes, and data warehouses.

The Data Engineer will work closely with data analysts, product engineers, platform teams, and other technical stakeholders to translate evolving data requirements into scalable engineering solutions. Since data reliability and performance are essential to analytics and customer-facing capabilities, the role requires strong attention to data integrity, pipeline performance, system availability, and maintainability. Engineers will participate in code reviews, technical design discussions, Agile development practices, documentation, and production troubleshooting. The opportunity is particularly relevant for professionals looking to strengthen their career in Big Data, Apache Spark, PySpark, distributed computing, cloud data engineering, data architecture, and real-time data processing while working on systems designed to support growing business and product data requirements.

Key Responsibilities

Build data pipelines: Design, develop, and maintain reliable data ingestion and processing pipelines that move information across different layers of the Chargebee data platform.
Develop ETL/ELT workflows: Create scalable extraction, transformation, and loading workflows that convert raw data into structured and usable datasets for analytics and product requirements.
Work with Apache Spark: Develop, maintain, and optimize distributed data-processing jobs using Apache Spark and related technologies to handle large-scale datasets efficiently.
Use Spark SQL: Write and optimize Spark SQL queries for data transformation, aggregation, filtering, and preparation of datasets required by downstream applications and analytical workloads.
Support streaming systems: Work with streaming and queue-based technologies such as Kafka to process event-driven data and contribute to reliable near-real-time data workflows.
Design data models: Develop efficient data models and transformations that support reporting, business intelligence, analytics applications, and customer-facing product features.
Maintain data quality: Implement practices that help maintain data accuracy, consistency, integrity, availability, and reliability throughout different stages of the data pipeline.
Troubleshoot production systems: Investigate failures and performance issues in production data pipelines and distributed processing systems, identify root causes, and implement appropriate fixes.
Optimize pipeline performance: Analyze processing workloads and improve query performance, resource utilization, transformation logic, and pipeline efficiency where required.
Collaborate with technical teams: Work with data analysts, product engineers, platform engineers, and other stakeholders to understand data requirements and deliver practical engineering solutions.
Participate in engineering practices: Contribute to code reviews, technical design discussions, Agile ceremonies, development planning, and collaborative problem-solving activities.
Maintain technical documentation: Document data pipelines, architecture decisions, workflows, dependencies, processing logic, and operational procedures to improve maintainability and knowledge sharing.

Requirements & Eligibility

Bachelor’s degree: Candidates should have a Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related technical discipline. Equivalent practical experience may also be considered.
1–2 years of experience: The position is intended for professionals with approximately 1–2 years of experience working with data processing workflows, data pipelines, or related data engineering technologies.
Apache Spark experience: Practical experience with distributed computing frameworks such as Apache Spark is important, particularly for candidates who have worked with large datasets or distributed processing workloads.
Strong SQL knowledge: Candidates should have strong SQL skills, including querying, filtering, joins, aggregations, transformations, subqueries, and working with relational datasets.
Programming proficiency: Proficiency in at least one programming language such as Python, Java, or Scala is required for developing data-processing applications and pipeline logic.
Data structures and models: A good understanding of data structures, data modeling, relational database concepts, schemas, and efficient data organization is important for building maintainable data systems.
Database experience: Candidates should have experience with relational databases such as PostgreSQL, MySQL, or comparable database technologies.
Production troubleshooting: Experience working with production systems and the ability to investigate, debug, and resolve technical problems in data pipelines or distributed environments is valuable.
Git and collaborative development: Candidates should be comfortable using Git-based workflows for source-code management, collaboration, branching, version control, and code review processes.
Agile development: Experience working in an Agile software-development environment and collaborating with engineers, analysts, product teams, and other stakeholders is expected.

Skills & Technologies

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