Senior Data Platform Engineer - NoSQL, Databricks & Kafka

Capgemini; Seattle (WA); On-site; USD 54,000 - 84,000; Full time

Job summary

Capgemini is seeking a hands-on Senior Data Platform Engineer in Seattle to build and operate enterprise-scale data platforms. You will migrate data, troubleshoot database and streaming issues, and automate platform operations with Python and IaC tooling.

The role emphasizes production support, on-call participation, and improving reliability, availability, and scalability of data platforms using Databricks, Kafka, and SQL databases.

Qualifications

• Strong NoSQL / distributed database knowledge.
• Experience with Kafka and data streaming.
• Extensive SQL database experience and performance tuning.
• Experience with platform reliability and production support.
• Familiarity with Terraform and CI/CD tooling.
• Programming in Python or similar scripting languages.
• Ability to implement monitoring and observability.

Responsibilities

• Build, operate, and support enterprise-scale Cassandra / NoSQL database platforms.
• Design and execute data migrations between source and target environments.
• Automate platform provisioning, deployment, monitoring, and operational activities.
• Support Kafka-based data streaming, troubleshoot producers, consumers, and topics.
• Work with Databricks, Spark/PySpark for large-scale data processing.
• Troubleshoot production database, streaming, and platform issues; participate in on-call shifts.

Skills

NoSQL

Kafka

SQL databases

Platform reliability

Python

Shell scripting

CI/CD

Monitoring

On-call support

Performance tuning

Tools

Databricks

Spark/PySpark

Terraform

Job description

Capgemini is seeking a hands-on Senior Data Platform Engineer in Seattle to build and operate enterprise-scale data platforms. You will migrate data, troubleshoot database and streaming issues, and automate platform operations with Python and IaC tooling.

The role emphasizes production support, on-call participation, and improving reliability, availability, and scalability of data platforms using Databricks, Kafka, and SQL databases.