Overview of the Company:

Jio Platforms Ltd. is a revolutionary Indian multinational tech company, often referred to as India's biggest startup, headquartered in Mumbai. Launched in 2019, it's the powerhouse behind Jio, India's largest mobile network with over 400 million users. But Jio Platforms is more than just telecom. It's a comprehensive digital ecosystem, developing cutting-edge solutions across media, entertainment, and enterprise services through popular brands like JioMart, JioFiber, and JioSaavn.

Join us at Jio Platforms and be part of a fast-paced, dynamic environment at the forefront of India's digital transformation. Collaborate with brilliant minds to develop next-gen solutions that empower millions and revolutionize industries.

Team Overview:

The Data Platforms Team is the launchpad for a data-driven future, empowering the Reliance Group of Companies. We're a passionate group of experts architecting an enterprise-scale data mesh to unlock the power of big data, generative AI, and ML modelling across various domains. We don't just manage data – we transform it into intelligent actions that fuel strategic decision-making. Imagine crafting a platform that automates data flow, fuels intelligent insights, and empowers the organization – that's what we do. 

Join our collaborative and innovative team, and be a part of shaping the future of data for India's biggest digital revolution! About the role.

Job Title: Data Engineer

Department/Business: Analytics COE

Location: Mumbai, Hyderabad, Gurgaon, Bangalore

Experience: 3 to 6 years of Experience

Remote/On-site/Hybrid: On - Site

Responsibilities:

• End-to-End Data Pipeline Development: Design, build, optimize, and maintain robust data pipelines across cloud, on-premises, or hybrid environments, ensuring performance, scalability, and seamless data flow.
• Reusable Components & Frameworks: Develop reusable data pipeline components and contribute to the team's data pipeline framework evolution.
• Data Architecture & Solutions: Contribute to data architecture design, applying data modelling, storage, and retrieval expertise.
• Data Governance & Automation: Champion data integrity, security, and efficiency through metadata management, automation, and data governance best practices.
• Collaborative Problem Solving: Partner with stakeholders, data teams, and engineers to define requirements, troubleshoot, optimize, and deliver data-driven insights.
• Mentorship & Knowledge Transfer: Guide and mentor junior data engineers, fostering knowledge sharing and professional growth.

Qualification Details:

• Education: Bachelor's degree or higher in Computer Science, Data Science, Engineering, or a related technical field.
• Core Programming: Excellent command of a primary data engineering language (Scala, Python, or Java) with a strong foundation in OOPS and functional programming concepts.
• Big Data Technologies: Hands-on experience with data processing frameworks (e.g., Hadoop, Spark, Apache Hive, NiFi, Ozone, Kudu), ideally including streaming technologies (Kafka, Spark Streaming, Flink, etc.).
• Database Expertise: Excellent querying skills (SQL) and strong understanding of relational databases (e.g., MySQL, PostgreSQL). Experience with NoSQL databases (e.g., MongoDB, Cassandra) is a plus.
• End-to-End Pipelines: Demonstrated experience in implementing, optimizing, and maintaining complete data pipelines, integrating varied sources and sinks including streaming real-time data.
• Cloud Expertise: Knowledge of Cloud Technologies like Azure HDInsights, Synapse, EventHub and GCP DataProc, Dataflow, BigQuery.
• CI/CD Expertise: Experience with CI/CD methodologies and tools, including strong Linux and shell scripting skills for automation.

Desired Skills & Attributes:

• Problem-Solving & Troubleshooting: Proven ability to analyze and solve complex data problems, troubleshoot data pipeline issues effectively.
• Communication & Collaboration: Excellent communication skills, both written and verbal, with the ability to collaborate across teams (data scientists, engineers, stakeholders).
• Continuous Learning & Adaptability: A demonstrated passion for staying up-to-date with emerging data technologies and a willingness to adapt to new tools.