Remote Data Engineer / Data Scientist - PySpark & MLOps

CNX; United States; Remote; USD 9,500 - 16,000; Full time

Job summary

Concentrix seeks an experienced Data Engineer / Data Scientist in Bangalore to design and deploy scalable data pipelines using Python, PySpark, and Azure Databricks. You will work as an individual contributor while collaborating with cross-functional teams to deliver data-centric solutions.

The role emphasizes building batch and streaming pipelines, ML project support, and MLOps practices with Spark, Delta Lake, and API development.

Qualifications

• 5–8 years of relevant experience in data engineering, data science, ML, or cloud analytics.
• Strong hands-on experience with Python, PySpark, and Azure Databricks.
• ML project experience and familiarity with MLOps practices.
• Experience building and debugging Spark-based applications.
• Ability to work independently and collaborate with project teams.

Responsibilities

• Develop scalable data processing solutions using Python, PySpark, and Azure Databricks.
• Build and maintain batch and streaming data pipelines.
• Develop and optimize Spark DataFrame-based transformations.
• Debug Spark jobs in Azure Databricks.
• Implement Delta Lake for data reliability and efficient querying.
• Develop APIs using Python or Scala for data and ML applications.
• Support ML projects, MLOps workflows, and model deployment.
• Work with Azure services for data ingestion, storage, security, and processing.
• Configure Databricks job clusters and notebook workflows.
• Validate data with DataFrame-based checks and collaborate with teams.

Skills

Python

PySpark

Azure Databricks

MLOps

ML deployment

Spark debugging

API development

Independent worker

Education

Bachelor’s or Master’s in CS/DS/Engineering

Tools

Databricks

Kafka

Azure Event Hubs

Azure Storage

GitHub

Job description

Concentrix seeks an experienced Data Engineer / Data Scientist in Bangalore to design and deploy scalable data pipelines using Python, PySpark, and Azure Databricks. You will work as an individual contributor while collaborating with cross-functional teams to deliver data-centric solutions.

The role emphasizes building batch and streaming pipelines, ML project support, and MLOps practices with Spark, Delta Lake, and API development.