Data Engineer

About the Role

The Data Platform team at Chargebee builds and maintains scalable data systems that power internal analytics, business intelligence, and customer-facing data features. As a

Data Engineer , you will work on building and maintaining reliable data pipelines across multiple layers of the data platform, including

data ingestion, distributed processing, transformation, and data serving .

You will collaborate closely with product engineers, analysts, and platform teams to ensure that data is ingested, processed, and made available efficiently for analytics and product use cases. This role provides opportunities to work with

modern data technologies and distributed systems , solving real-world data challenges at scale.

The team operates in a

fast-paced and collaborative environment , building reliable and scalable infrastructure that supports Chargebee’s growing data needs.

What You Will Work On

As a Data Engineer, you will contribute to the development and evolution of Chargebee’s data platform. This includes working on systems responsible for ingesting large volumes of data, processing and transforming it using distributed systems like

Apache Spark , and making it available for analytics and customer-facing products.

The role provides exposure to:

Large-scale data ingestion and processing pipelines Streaming and event-driven architectures Distributed data processing frameworks Cloud-based data infrastructure Building and maintaining data lake and data warehouses Building data capabilities powering both internal analytics and customer-facing products

Key Responsibilities

Design, build, and maintain

data ingestion and processing pipelines

across multiple layers of the data platform. Develop and optimize

ETL/ELT workflows

to support scalable and reliable data processing. Build and maintain distributed data processing jobs using frameworks such as

Apache Spark . Work with

streaming and queue-based systems

such as Kafka to process event-driven data. Design efficient

data models and transformations

to support analytics and product use cases. Ensure high standards of

data reliability, integrity, and performance

across the data platform. Troubleshoot and debug

production data pipelines and distributed systems . Collaborate with

data analysts, product teams, and engineers

to support evolving data requirements. Participate in

code reviews, design discussions, and agile development processes . Document

data pipelines, architecture decisions, and platform workflows .

Minimum Qualifications

Bachelor’s degree in

Computer Science, Mathematics, Engineering, or a related technical field , or equivalent practical experience. 1–2 years of experience

working with data processing workflows and pipelines. Experience working on

production systems , including troubleshooting and debugging technical issues. Experience with

distributed computing system

such as Apace Spark. Experience using

Git workflows

in collaborative development environments. Strong knowledge of

SQL . Proficiency in at least one programming language such as

Java, Python, or Scala . Good understanding of

data structures, data models, and relational database concepts . Experience with relational databases such as

PostgreSQL, MySQL, or similar . Experience working in an

Agile development environment .

Good-to-Have Qualifications

Experience working within the

AWS ecosystem . Experience building data pipelines using

Apache Spark . Familiarity with

distributed data processing frameworks

and large-scale data systems. Understanding of

data warehouse and data lake architectures . Strong

technical communication and problem-solving skills . Ability to

investigate and debug issues across distributed systems . Knowledge / prior experience on open table formats like - deltalake, iceberg or hudi