Senior Data Engineer
Jiostar Open · verified Oct 24, 2026 At a glance
Senior Data Engineering role at Jiostar. Mumbai.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build batch and near-real-time pipelines, handling data from ingestion through transformation and storage
- Use distributed processing to create scalable data solutions, including with Spark
- Prepare datasets for analytics, dashboards and reporting across different use cases
- Track pipeline speed, data freshness and reliability, then investigate and fix problems
- Turn stakeholder data needs into technical solutions and apply governance and security practices
What you bring
- Five to seven years working hands-on in data engineering or big data roles
- Practical experience with Spark and building data solutions on Google Cloud, including BigQuery or another listed service
- Strong SQL and Python skills, plus experience handling large datasets
- A computer science, engineering, mathematics or related degree, or equivalent practical experience
Who this fits
This senior role suits someone comfortable with large-scale data environments and distributed processing. You’ll work with analytics, business and platform stakeholders, and take part in code reviews and design discussions. The role is based in Mumbai, India.
From the employer
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
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