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Profound

Software Engineer, Data Platform

New York, New York$140k–$260kfulltimemidAdded 1 month ago

About this role

Profound, a $1B AI marketing platform backed by top-tier VCs, seeks a Software Engineer to design and build scalable data infrastructure supporting analytics, ML, and engineering teams. You'll architect platforms using modern data stack tools while enabling data scientists and engineers to drive insights and AI-powered features.

What you'll do

  • Design platform infrastructure for data ingestion, transformation, quality monitoring, and observability
  • Build governance, access control, and data quality systems that scale organizationally
  • Architect systems using Snowflake, ClickHouse, AWS, dbt, and Dagster
  • Enable MLOps by creating infrastructure for model development and production deployment
  • Lead technical decisions on platform architecture and data patterns
  • Collaborate with product, engineering, and data teams to deliver impactful solutions

What they're looking for

  • Large-scale distributed data systems design and operation
  • Analytics/lakehouse platforms (ClickHouse, Snowflake, Tinybird, Iceberg)
  • Cloud infrastructure (AWS, GCP, or Azure)
  • Data governance, reliability, and secure operations
  • Data orchestration and transformation tools (dbt, Dagster)
  • MLOps and machine learning workflows
  • Cross-team communication and influence
  • Problem-solving in fast-paced environments

Benefits

  • Competitive base salary ($140,000–$260,000 depending on location)
  • Meaningful equity package
  • Full benefits and perks
  • Visa sponsorship available
  • Autonomy and trust in a fast-moving culture
  • Opportunity to shape data platform at early stage of growth
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Profound

Profound builds an AI-native marketing platform used by Fortune 500 companies that automates lead qualification, scoring, and routing while capturing prospect intent signals. The company is hiring Infrastructure Engineers, Data Engineers, Design Engineers, GTM Engineers, and Talent Engineers to scale its cloud systems, data pipelines, user experiences, and internal recruiting automation.

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Likely interview questions

  • Walk us through a time you designed a data platform or infrastructure that served multiple teams (data scientists, analytics engineers, ML engineers). What were the key architectural decisions and trade-offs?
  • Describe your experience with Snowflake, ClickHouse, or similar analytics/lakehouse technologies. How have you optimized for scale and cost in production?
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