SDE II - Data Engineer
InMobi (company site, opens in a new tab)2–5 yrsLucknowOn-siteDirect career pageEst. 10–18 LPA
Posted 2h ago
Reach people at InMobi
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at InMobi you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Build, maintain, and optimize batch and streaming data pipelines for programmatic advertising events and analytics.
- Develop data solutions supporting distributed processing frameworks (Apache Spark/PySpark), cloud data platforms, and analytical storage systems.
- Collaborate with backend, product, and ML engineering teams to ensure data pipeline reliability, performance, and quality monitoring.
Minimum Requirements
- 2–5 years of experience in software or data engineering, or big data development.
- Bachelor's or Master's degree in Computer Science, Engineering, or related technical field.
- Strong programming skills in Python and SQL with experience in Apache Spark/PySpark or comparable distributed data processing frameworks.
- Experience with batch and streaming data pipeline development in production; exposure to Apache Kafka and workflow orchestration tools like Apache Airflow is required.
Ideal Candidate Profile
- Experience working on high-volume, event-driven data systems particularly in ad-tech or programmatic advertising domains is preferred.
- Demonstrates expertise in optimizing data pipelines for scalability, reliability, and query performance in distributed and cloud-based architectures.
- Proficient in collaborating with cross-functional teams including ML engineering, with a strong understanding of data quality, monitoring, and production troubleshooting.