Data Engineer

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

Role overview

A mid-level Data Engineer position focused on building and scaling cloud-native data infrastructure that powers analytics, machine learning, and product-facing features. The role sits at the intersection of platform engineering and applied data work, contributing to both the underlying systems and the way data is consumed by analysts, AI engineers, and product teams.

Responsibilities

• Build and maintain data pipelines that ingest from internal databases, SaaS sources, and streaming systems, serving analytics, ML workloads, and product applications
• Evolve the data platform alongside cloud platform engineers using infrastructure-as-code and Kubernetes-based deployments
• Design and implement data services and APIs that expose trusted data to product applications, bridging analytical and operational systems
• Own data quality, observability, monitoring, testing, and alerting so issues are surfaced early
• Partner with AI engineers, data scientists, analysts, and product teams to translate data needs into well-designed solutions
• Uphold strong data privacy, security, and compliance practices across all systems
• Maintain comprehensive documentation of data flows, models, and architecture

Requirements

• Three or more years building and shipping production-grade data systems
• Strong SQL skills plus proficiency in Python or another object-oriented language such as Java or Scala
• Familiarity with cloud-native infrastructure, ideally including Terraform and Kubernetes or comparable IaC and container orchestration tools
• Hands-on experience with data pipeline tools such as Airflow and dbt, with an eye for performance and reliability
• Experience designing data APIs or services, or a strong interest in working across the analytical and operational boundary
• Thoughtful approach to data quality, privacy, and security, with strong collaboration skills across multidisciplinary teams

Nice to have

• A pragmatic, curious mindset with the judgment to know when a new tool is warranted versus when to stick with proven approaches
• Ability to ship medium-sized features independently while contributing to broader team objectives