Data Engineer

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.

Company Overview

A rapidly growing AI infrastructure company is building the foundational layer between human expertise and frontier AI models. The organization enables millions of domain experts to contribute to AI advancement while developing benchmarking and enterprise solutions that help organizations capture and scale human expertise through intelligent systems. With strong market momentum, profitability, and global operations, the company is focused on shaping the future of work and artificial intelligence.

The organization is seeking a Data Engineer to support its Data function by creating and maintaining the data infrastructure that powers Data Science, Engineering, Product, and business teams. This role is ideal for professionals who bring a full-stack perspective to data engineering and enjoy building scalable, reliable systems that enable data-driven decision-making.

The successful candidate will focus on data reliability, availability, and timeliness while collaborating closely with data scientists, engineers, and cross-functional stakeholders. The role offers the opportunity to work on critical infrastructure supporting AI-focused products and services.

Key Responsibilities

• Build robust data pipelines to ingest, transform, and consolidate information from multiple sources, including databases, analytics platforms, and SaaS applications.
• Design and maintain dbt models and transformations that standardize and unify disparate datasets into production-ready schemas.
• Implement scalable and fault-tolerant data workflows using modern data stack technologies.
• Partner with engineering, data science, product, and business teams to ensure data accessibility, accuracy, and usability.
• Own data quality and reliability across the entire data lifecycle, from ingestion through consumption.
• Monitor, optimize, and scale data pipelines to support growing business and operational requirements.
• Contribute to the continuous improvement of data infrastructure, governance, and operational processes.

Required Qualifications

• Proven experience in data engineering with strong expertise in SQL, Python, and modern data stack technologies.
• Experience building and maintaining large-scale ETL/ELT pipelines across diverse data sources.
• Strong understanding of data modeling, schema design, and transformation best practices.
• Familiarity with data governance, monitoring frameworks, and data quality assurance processes.
• Ability to work effectively with cross-functional teams, including engineering, product, operations, and analytics stakeholders.
• Strong problem-solving skills and the ability to manage complex data workflows in a fast-paced environment.

Preferred Qualifications

• Experience with tools such as Fivetran, dbt, Snowflake, or similar modern data platforms.
• Previous experience supporting machine learning workflows, AI systems, or analytics platforms.
• Knowledge of scalable data architectures and cloud-based data infrastructure.

Why Join

• Contribute to technology that supports the development and evaluation of advanced AI models.
• Gain exposure to cutting-edge advancements in artificial intelligence and machine learning.
• Work on impactful projects spanning both infrastructure and research-adjacent initiatives.
• Enjoy significant ownership opportunities within a fast-growing and innovative environment.
• Collaborate with highly talented teams working at the forefront of AI and data technology.

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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