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