Lead Cloud Data Platform Engineer

What's the Job?

Design, develop, and operationalize modern AI-enabled data capabilities on Google Cloud platform.

Create and support data ingestion, transformation, and distribution processes for large-scale data applications.

Leverage AI and agentic frameworks to automate data management, governance, and consumption capabilities.

Collaborate with cross-functional teams including principal engineers, product managers, and data engineers to roadmap and deliver key data solutions.

Drive innovation by exploring and implementing new cloud and data technologies to enhance the cybersecurity data ecosystem.

What's Needed?

• Recent hands-on experience with AI tools such as LangChain, LangGraph/ADK, agentic frameworks, RAG, GraphRAG, and MCP for data capabilities.
• Minimum of 5 years of experience in data engineering, including working with cloud data solutions like Spark-based ingestion and processing.
• At least 3 years of experience with Data Lakehouse architecture and design, utilizing Python, pySpark, Kafka, Airflow, Google Cloud Storage, BigQuery, DataProc, and Cloud Composer.
• Proficiency in developing data flows using Kafka, Flink, and Spark streaming technologies.
• Strong problem-solving skills, adaptability, and ability to work effectively within a collaborative, agile environment.

About the Role

The Lead Cloud Data Platform Engineer will be part of the Cyber Security Data Ecosystem supporting innovative data engineering and cloud analytics initiatives.

The ideal candidate will demonstrate strong technical expertise, collaborative spirit, and a passion for pioneering new technologies, which will align successfully in the organization.

Requirements

Minimum of 5 years of experience in data engineering, including working with cloud data solutions like Spark-based ingestion and processing.

At least 3 years of experience with Data Lakehouse architecture and design, utilizing Python, pySpark, Kafka, Airflow, Google Cloud Storage, BigQuery, DataProc, and Cloud Composer.

Proficiency in developing data flows using Kafka, Flink, and Spark streaming technologies.

Strong problem-solving skills, adaptability, and ability to work effectively within a collaborative, agile environment.

Qualifications

Recent hands-on experience with AI tools such as LangChain, LangGraph/ADK, agentic frameworks, RAG, GraphRAG, and MCP for data capabilities.

Skills

• AI tools such as LangChain, LangGraph/ADK, agentic frameworks, RAG, GraphRAG, and MCP for data capabilities.
• Data engineering, including working with cloud data solutions like Spark-based ingestion and processing.
• Data Lakehouse architecture and design, utilizing Python, pySpark, Kafka, Airflow, Google Cloud Storage, BigQuery, DataProc, and Cloud Composer.
• Developing data flows using Kafka, Flink, and Spark streaming technologies.
• Problem-solving skills, adaptability, and ability to work effectively within a collaborative, agile environment.

Benefits

Opportunity to work on cutting-edge cloud and AI technologies within a dynamic team.

Engagement in impactful projects that enhance cybersecurity and data analytics capabilities.

Collaborative work environment fostering innovation and continuous learning.

Potential for professional growth and development in a forward-thinking organization.

Participation in a mission-driven organization committed to technological excellence.