Cloud Data Engineer — Real-Time Pipelines & Spark

Jobo; United States; Remote; USD 90,000 - 130,000; Full time

Benefits offered by this job

Private medical care

Dental care

Life insurance

Cafeteria benefits

Sharing costs of sports activities

Job summary

Cognizant is seeking a talented Data Engineer to design, build, and maintain scalable data platforms and pipelines that support critical business operations in a cloud-native environment. You will work with batch and real-time data flows, leveraging Spark, Kafka, and distributed systems to deliver impactful data products.

You will also deploy and manage containerized data services using Docker and Kubernetes, ensuring data quality, governance, and security across platforms while collaborating

Qualifications

• Strong experience as a Data Engineer or similar data-focused role.
• Proficient in Python, Java, and/or Kotlin.
• Hands-on with Spark, Hadoop, Kafka, Hive, Airflow, and SQL.
• Understanding of distributed systems and scalable data processing.
• Knowledge of containerization and orchestration (Docker, Kubernetes).

Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines.
• Build and optimize data processing solutions using Spark, Kafka, and distributed frameworks.
• Develop, deploy, and manage containerized applications and data services (Docker, Kubernetes).
• Establish data quality, governance, security, and compliance across platforms.
• Collaborate with data scientists, analysts, and product teams to deliver data products.

Skills

Python

Java

Kotlin

Apache Spark

Apache Hadoop

Kafka

Hive

Airflow

SQL

Distributed systems

Tools

Docker

Kubernetes

Job description

Cognizant is seeking a talented Data Engineer to design, build, and maintain scalable data platforms and pipelines that support critical business operations in a cloud-native environment. You will work with batch and real-time data flows, leveraging Spark, Kafka, and distributed systems to deliver impactful data products.

You will also deploy and manage containerized data services using Docker and Kubernetes, ensuring data quality, governance, and security across platforms while collaborating