Senior Data Engineer
Job Description
🆔Job ID: 340788
🏢Company: Oracle
📍Location: Bangalore, Karnataka, India 🇮🇳
💼Job Type: Full-Time | Regular Employee 💻
🏢Work Mode: Not Specified 🏢
⏳Experience: 3–5+ Years 👨💻
🎓Education: B.E. in Computer Science, Computer Engineering, or related field 🎓
💰Expected Salary: ₹20 – ₹35 Lakhs Per Year 💸
📊Job Category: Data Engineering, Information Technology, Engineering, Software Development, Distributed Systems, AI/ML, Cloud, Data Pipelines 🌐
🌐Website: Website
📞Contact: Contact us
🌟Data is only powerful when you can move it, trust it, and use it at scale.
Millions of records.
Multiple data sources.
Distributed systems.
Cloud infrastructure.
And increasingly, AI and machine learning sitting on top of it all.
Someone has to build the systems that make this possible.
That’s where this opportunity comes in.
Oracle is hiring a Senior Data Engineer in Bengaluru to build and operate large-scale data platforms within a distributed, multi-tenant cloud environment.
If you enjoy solving complex engineering problems and building systems that need to be fast, reliable, scalable, and resilient, this role deserves your attention.
🌟About Oracle:
Oracle is a global technology company providing cloud infrastructure, enterprise applications, databases, and AI-powered technology solutions. 🌎☁️
The company brings together data, infrastructure, applications, and engineering expertise to help organizations solve some of their most complex technology challenges.
With AI increasingly embedded across its products and services, Oracle continues to invest heavily in cloud and data technologies that support businesses and critical industries worldwide.
For engineers, that means working on systems where scale, reliability, and performance aren’t optional.
💡About the Role:
As a Senior Data Engineer, you’ll work on massive-scale engineering platforms operating within a distributed, multi-tenant cloud environment.
Your work will span data pipelines, distributed computing, microservices, APIs, data governance, data quality, storage, and scalable data solutions.
You’ll translate business requirements into technical specifications, design reusable data pipelines, optimize data processing, and ensure that data remains accurate, secure, compliant, and accessible.
The role also gives you the opportunity to work with technologies such as Java or Scala, Spring Boot, Spark, MapReduce, Hive, databases, CI/CD, and cloud storage.
This isn’t just about moving data from A to B.
It’s about engineering the infrastructure that makes data useful at massive scale.
📌Key Responsibilities:
📌 Identify data requirements and business objectives in collaboration with cross-functional teams. 🎯
📌 Design and build scalable data pipelines from multiple data sources. 🔄
📌 Analyze, design, and troubleshoot complex data flows based on business requirements. 🔍
📌 Translate business requirements into clear technical specifications. 💻
📌 Optimize indexing, queries, and data collection processes for performance. ⚡
📌 Build reusable and generic data pipelines for efficient data collection and extraction. 🛠️
📌 Profile data sources to identify potential issues before pipeline implementation. 📊
📌 Define success and failure thresholds for data collection pipelines. ✅
📌 Implement data governance policies covering consistency, integrity, accuracy, reliability, and retention. 🔐
📌 Protect sensitive information by implementing appropriate PII and PHI data-redaction practices. 🛡️
📌 Ensure data handling complies with applicable privacy, security, and industry requirements. ⚖️
📌 Implement rigorous data validation and integrity checks. 🔎
📌 Automate data validation and governance processes. ⚙️
📌 Design and optimize automated, reusable, and scalable data pipeline architectures. 🚀
📌 Implement appropriate storage solutions for processed data and analytics. ☁️
📌 Manage day-to-day data pipeline and storage operations. 🗄️
📌 Develop, maintain, test, and debug scalable data solutions in an Agile environment. 👨💻
📌 Write production-ready code and independently perform testing and debugging. 🧪
📌 Collaborate with engineers and stakeholders to deliver reliable and cost-effective solutions. 🤝
🎯Requirements:
🎯 5+ years of coding experience in Java or Scala. ☕
🎯 5+ years of experience building microservices/APIs using Spring Boot. ⚙️
🎯 5+ years of distributed computing experience with technologies such as Spark, MapReduce, or Hive. 📊
🎯 Experience building high-performance, resilient, scalable, and well-engineered systems. 🚀
🎯 Strong understanding of object-oriented design, distributed systems, multithreading, databases, and full-stack software concepts. 🧠
🎯 Experience with CI/CD and modern software development best practices. 🔄
🎯 Experience with cloud data storage technologies such as HDFS and S3 is preferred. ☁️
🎯 Experience with instrumentation and logging systems. 📈
🎯 Multi-cloud networking experience is preferred. 🌐
🎯 B.E. in Computer Science, Computer Engineering, or a related discipline. 🎓
🎯 Strong analytical and problem-solving abilities. 🔍
🎯 Ability to independently manage technical deliverables and project timelines. 📅
🎯 Strong collaboration and communication skills. 💬
🎯 Willingness to continuously learn new technologies and engineering practices. 📚
🌟Preferred Skills:
✅ Java / Scala ☕
✅ Spring Boot & Microservices ⚙️
✅ Apache Spark / MapReduce / Hive 📊
✅ Distributed Systems 🌐
✅ Data Pipeline Engineering 🔄
✅ Cloud Data Storage ☁️
✅ HDFS / Amazon S3 🗄️
✅ CI/CD & DevOps 🔧
✅ Data Governance & Quality 🔐
✅ Multi-Cloud Networking 🌎
🌟Why Join Oracle?
✅ Work on massive-scale distributed engineering platforms. 🌎
✅ Build systems supporting AI/ML and modern cloud technologies. 🤖
✅ Gain experience with complex data processing and pipeline architectures. 📊
✅ Work with distributed computing technologies and cloud infrastructure. ☁️
✅ Solve engineering challenges that require scalability and resilience. 🚀
✅ Collaborate with engineers and cross-functional teams globally. 🤝
✅ Contribute to engineering best practices and continuous improvement. 📈
✅ Join a company investing heavily in AI and cloud innovation. 🌟
🔥Career Insight:
A data engineer’s job isn’t simply to move data.
It’s to make data usable, reliable, secure, and scalable.
At small scale, almost anything works.
At massive scale, everything becomes an engineering problem.
Latency matters.
Failures matter.
Data quality matters.
Security matters.
And architecture matters.
That’s why distributed systems experience can become such a powerful career advantage.
Because when you learn how to build systems that keep working even when everything around them gets bigger, you’re no longer just writing pipelines.
You’re engineering the foundation that other products, teams, and AI systems depend on.
