Data Engineer, Platform

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Company Overview

A growing AI technology company is developing ambitious AI products and requires a reliable data platform to support agent runs, testing, evaluations, analytics, and remediation workflows. The organization is seeking an experienced data engineering professional who can build dependable pipelines and data systems that enable AI and product teams to work with trusted, high-quality datasets.

The ideal candidate will have hands-on experience building production data pipelines that support AI, machine learning, or evaluation workloads. The role is well suited for professionals with backgrounds in Analytics Engineering, ETL Development, Big Data Engineering, or Data Platform Engineering.

About the Role

The organization is seeking a Data Engineer, Platform to build the pipelines that transform raw agent runs, test results, and remediation outcomes into reliable and trustworthy data. The role will own the data layer supporting the organization’s control plane, including streaming and batch ingestion, warehouse models for evaluations and analytics, data quality checks, and lineage.

The successful candidate will have experience building pipelines that other teams rely on daily and will approach data quality as a core engineering responsibility rather than a downstream cleanup task.

Compensation

• Competitive salary.
• Meaningful equity.

Key Responsibilities

• Design and build ingestion pipelines for agent runs, defect reproductions, and remediation outcomes.
• Model and maintain warehouse tables that support evaluations, analytics, and product reporting.
• Build streaming and batch data paths that maintain data freshness without compromising correctness.
• Own pipeline orchestration end to end, including scheduling, dependencies, retries, and backfills.
• Integrate data quality checks, testing, and alerting into production pipelines.
• Track data lineage to ensure reported metrics can be traced back to their source evidence.
• Partner with AI and product engineering teams to transform raw signals into reliable datasets.
• Own the cost, performance, and reliability of the production data platform.
• Ensure data pipelines remain scalable, maintainable, and dependable as platform requirements evolve.

Required Qualifications

• Experience building and operating production data pipelines.
• Strong SQL skills and experience modeling data in a warehouse environment.
• Proficiency in Python or a similar programming language for data engineering.
• Working knowledge of orchestration tools such as Airflow, Dagster, or Prefect.
• Understanding of data quality, testing, and schema evolution.
• Strong judgment when balancing data freshness, cost, and correctness.
• Clear written and verbal communication skills.
• Strong ownership of systems and solutions developed in production.
• Hands-on experience building data pipelines that support AI, ML, or evaluation workloads.

Preferred Qualifications

• Experience with streaming systems such as Kafka, Kinesis, or Pub/Sub.
• Experience with dbt, Snowflake, BigQuery, Databricks, or similar data technologies.
• Experience preparing datasets for evaluations, fine-tuning, or model training.
• Experience establishing or scaling a data platform within an early-stage company.
• Experience working closely with AI and product engineering teams.
• Experience designing systems for high-volume data ingestion and processing.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
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