Data Platform Engineer - Real-Time Analytics (Remote)

Q One Inc.; Los Angeles (CA); On-site; USD 170,000 - 230,000; Full time

Job summary

Q One is hiring a Senior Data Platform Engineer to design, build, and operate the infrastructure powering real-time analytics and AI systems. You will ensure scalable, secure data infrastructure capable of handling billions of records and large-scale workloads.

Responsibilities include building cloud-native data pipelines, optimizing storage and compute, and driving reliability and observability across the platform. Collaboration with data and engineering teams is essential.

Qualifications

• Experience building scalable data platforms for real-time analytics.
• Proficient in cloud-native architectures and data pipelines.
• Strong knowledge of distributed systems and data security.

Responsibilities

• Design and build scalable data platform infrastructure.
• Develop real-time and batch data processing pipelines.
• Build cloud-native architectures for market intelligence workloads.
• Optimize storage, compute, and data retrieval performance.
• Implement data orchestration and workflow automation.
• Design highly available distributed systems.
• Manage observability, monitoring, alerting, and reliability engineering.
• Support AI, machine learning, and quantitative research data needs.
• Collaborate with market data, financial data, and engineering teams.

Skills

Kafka

Spark

Flink

Airflow

ETL/ELT Architecture

Streaming Data Pipelines

AWS

Azure

GCP

Kubernetes

Docker

Terraform

Infrastructure as Code

Snowflake

Databricks

ClickHouse

PostgreSQL

Redis

Delta Lake

Apache Iceberg

Python

SQL

Java

Scala

Go

Rust

Monitoring/Observability

CI/CD

Platform Security

Disaster Recovery

High Availability

Tools

Snowflake

Databricks

ClickHouse

PostgreSQL

Redis

Delta Lake

Apache Iceberg

Job description

Q One is hiring a Senior Data Platform Engineer to design, build, and operate the infrastructure powering real-time analytics and AI systems. You will ensure scalable, secure data infrastructure capable of handling billions of records and large-scale workloads.

Responsibilities include building cloud-native data pipelines, optimizing storage and compute, and driving reliability and observability across the platform. Collaboration with data and engineering teams is essential.