Data Engineer / Data Scientist
Concentrix Corporation (company site, opens in a new tab)5–8 yrsBengaluruOn-siteDirect career pageEst. 12–20 LPA
Posted 6h ago
Reach people at Concentrix Corporation
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 Concentrix Corporation 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 and operate scalable batch and streaming data pipelines and Spark transformations using Python, PySpark, and Azure Databricks.
- Own Spark job debugging and optimization, Delta Lake implementation, data validation, and Databricks workflow and cluster configuration.
- Support machine-learning projects, MLOps workflows, APIs, and model deployment across Azure data services.
Minimum Requirements
- Work Experience Required: 5–8 years in data engineering, data science, machine learning, or cloud analytics.
- Must-have hands-on skills: Python, PySpark, Azure Databricks, MLOps, Spark application development and debugging; practical machine-learning project experience and basic model-deployment understanding.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Information Technology, or a related discipline.
- Work arrangement: Remote is stated, while an India-Bangalore location is also listed; onsite expectations are not explicitly mentioned in the JD.
Ideal Candidate Profile
- Has hands-on experience delivering production-oriented batch and streaming pipelines, ideally using Event Hubs, Kafka, or IoT data sources.
- Can independently own data and Spark deliverables while coordinating with technical and business teams.
- Fits a cloud analytics and ML delivery environment, with useful additional exposure to Delta Lake, Azure services, APIs, CI/CD, MLflow, and production monitoring.