Cloud Data Platform Engineer - Pipelines, APIs & Analytics

prolific; United States; Remote; USD 110,000 - 150,000; Full time

Job summary

Prolific is seeking a mid-level Data Engineer to build and scale cloud-native data infrastructure powering analytics, ML, and product features. You will work at the intersection of platform and data engineering, shaping systems used by analysts, AI engineers, and product teams.

You will own data pipelines, collaborate with cloud platform engineers, and contribute to data quality, observability, and security across the data stack, documenting flows and architectures.

Qualifications

• Three or more years building and shipping production-grade data systems.
• Strong SQL skills with proficiency in Python or Java/Scala.
• Experience with cloud-native infrastructure and IaC tools like Terraform and Kubernetes.
• Hands-on experience with Airflow and dbt for data pipelines.
• Experience designing data APIs or services; comfortable across analytical and operational boundaries.
• Focus on data quality, privacy and security, and collaboration across teams.

Responsibilities

• Build and maintain data pipelines from internal databases, SaaS sources, and streaming systems.
• Evolve data platform with IaC and Kubernetes deployments.
• Design and implement data services and APIs exposing trusted data to products.
• Own data quality, observability, monitoring, testing, and alerting.
• Partner with AI engineers, data scientists, analysts, and product teams.

Skills

SQL

Python

Java/Scala

Data APIs

Collaboration

Tools

Airflow

dbt

Terraform

Kubernetes

Job description

Prolific is seeking a mid-level Data Engineer to build and scale cloud-native data infrastructure powering analytics, ML, and product features. You will work at the intersection of platform and data engineering, shaping systems used by analysts, AI engineers, and product teams.

You will own data pipelines, collaborate with cloud platform engineers, and contribute to data quality, observability, and security across the data stack, documenting flows and architectures.