Data Platform Engineer for AI Analytics & MLOps

Profound; New York (NY); Hybrid; USD 140,000 - 260,000; Full time

Benefits offered by this job

Equity

Benefits package

Job summary

Profound is seeking a Software Engineer, Data Platform to design, build, and scale the infrastructure that powers data across our organization. You will architect scalable platforms and create tools for data scientists, analytics engineers, and ML engineers, enabling analytics, experimentation, and ML-powered features.

This is a great opportunity to work on a robust data platform in a fast-paced environment, shaping data workflows and governance while enabling cross-team collaboration.

Qualifications

• Experience building platforms for engineers and data scientists.
• Experience operating large-scale distributed data systems.
• Proficiency with lakehouse technologies (Snowflake, ClickHouse, Iceberg, etc.).
• Strong cloud experience (AWS, GCP, or Azure).
• Knowledge of data governance, reliability, and secure data operations.

Responsibilities

• Design and build platform infrastructure that supports ML, analytics, and software engineers, plus data scientists.
• Develop tools for data ingestion, transformation, quality, and observability.
• Architect scalable, reliable systems using Snowflake, ClickHouse, AWS, dbt, and Dagster.
• Build governance, access control, and data quality systems that scale.
• Enable MLOps with infrastructure for model development and productionization.
• Drive technical decisions around platform architecture and data patterns.
• Collaborate with product, engineering, and data teams to deliver impactful solutions.

Skills

Platform architecture

Distributed data systems

Cloud platforms (AWS/GCP/Azure)

Data governance

MLOps interest

Communication

Tools

Snowflake

ClickHouse

dbt

Dagster

Tinybird

Iceberg

AWS

GCP

Azure

Job description

Profound is seeking a Software Engineer, Data Platform to design, build, and scale the infrastructure that powers data across our organization. You will architect scalable platforms and create tools for data scientists, analytics engineers, and ML engineers, enabling analytics, experimentation, and ML-powered features.

This is a great opportunity to work on a robust data platform in a fast-paced environment, shaping data workflows and governance while enabling cross-team collaboration.