Senior Data Engineer

Jiostar; Mumbai City; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Jiostar is seeking a Data Engineer to design, build, and maintain scalable data pipelines and platforms supporting analytics, reporting, and downstream applications. You will work across ingestion, processing, storage, and consumption with a focus on data quality and operational stability.

You will collaborate with analytics teams, business stakeholders, and platform teams to enable timely and accurate data availability across multiple use cases, implement ETL/ELT workflows, and uphold data

Qualifications

• Bachelor's or Master’s in CS/Engineering/Math or related field.
• 5–7 years of hands-on data engineering / big data experience.
• Experience in large-scale data environments.

Responsibilities

• Design, develop, and maintain batch and near real-time data pipelines.
• Ingest, ETL/ELT workflows from multiple sources and ensure data quality.
• Build and optimize scalable data processing using Spark.
• Develop and manage datasets for analytics, dashboards, and reporting.
• Monitor pipeline performance, data freshness, and reliability.
• Translate data requirements into technical solutions for stakeholders.
• Ensure adherence to data governance, security, and engineering best practices.
• Participate in code reviews, design discussions, and continuous improvement.

Skills

Data pipelines

ETL/ELT workflows

Distributed systems

Data governance

Stakeholder collaboration

Performance optimization

Education

Bachelor’s or Master’s degree in CS/Engineering/Math

Tools

Apache Spark

Google Cloud Platform (GCP)

BigQuery

Dataproc

Dataflow

Pub/Sub

Python

SQL

Job description

Job Summary:

The Data Engineer will be responsible for designing, building, and maintaining scalable and reliable data pipelines and data platforms to support analytics, reporting, and downstream applications. The role involves working across the entire data lifecycle - ingestion, processing, storage, and consumption with a strong focus on data quality, performance, and operational stability. The engineer will work closely with analytics teams, business stakeholders, and platform teams to enable timely and accurate data availability across multiple use cases.

About the team:

The Data Platform Team is a group of data engineers, analytics developers, and QA specialists building a scalable, low-latency data lakehouse that enables PRS, Finance, and Marketing teams to make data-driven decisions.

We process tens of millions of events every week and have designed a resilient, future-ready platform that scales with the organization’s growing analytical needs while ensuring high performance and reliability.

Key responsibilities:

• Design, develop, and maintain batch and near real-time data pipelines using modern data engineering frameworks and tools


• Implement and manage data ingestion, ETL/ELT workflows, and data transformations from multiple internal and external data sources


• Build and optimize scalable data processing solutions using distributed computing frameworks (e.g., Spark)


• Develop and manage datasets for analytics, dashboards, and reporting use cases


• Monitor pipeline performance, data freshness, and reliability; proactively identify and resolve issues


• Work with stakeholders to understand data requirements and translate them into technical solutions


• Ensure adherence to data governance, security, and best engineering practices


• Participate in code reviews, design discussions, and continuous improvement initiatives

Skills and attributes for success:

• Strong experience with data engineering and data pipelining concepts


• Hands-on experience with distributed data processing frameworks such as Apache Spark


• Experience building data solutions on Google Cloud Platform (GCP), includingcomponents such as Google Cloud Storage (GCS), BigQuery, Dataproc / Dataflow, Pub/Sub


• Strong SQL skills and experience working with large datasets


• Proficiency in Python (Java/Scala is a plus)


• Experience integrating data from multiple heterogeneous data sources


• Solid understanding of distributed systems, parallel processing, and performance optimization

Preferred education and experience:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience)


• 5–7 years of hands-on experience in data engineering / big data roles


• Strong experience working in complex, large-scale data environments