Data Engineer
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Job Description
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About Troveo
Troveo builds the data platform that AI labs and model builders need to train the next generation of models. We have created the world's largest licensed platform of scarce, proprietary data for AI, spanning video, audio, text, and business workflows.
Troveo indexes, enriches, and packages this high-quality data into formats ready for training, fine-tuning, evaluation, and agentic use cases. Backed by top investors, we’re a small, high-impact team solving one of the biggest bottlenecks in AI development.
Role Overview
We are seeking a versatile, hands-on Data Engineer to build and maintain a scalable analytics data warehouse while contributing to data modeling, performing data analysis, and ensuring reliable data delivery to downstream teams and systems. This hybrid role combines core data engineering responsibilities with data modeling, analytics, and operational support. You will own the full analytics data lifecycle, from ingestion and transformation to modeling, quality assurance, and timely delivery, while partnering closely with software engineers and business stakeholders.
Key Responsibilities
Data Pipeline Engineering
Design, build, and maintain ELT/ETL data pipelines (batch and streaming), optimizing for performance, reliability, scalability, and cost.
Design and implement conceptual, logical, and physical data models (including dimensional modeling, star/snowflake schemas).
Build and maintain transformation layers using modern tools (e.g., dbt) to create clean, well-documented, analytics-ready datasets.
Apply data modeling best practices, versioning, testing, and documentation to ensure consistency and reusability.
Data Analysis & Reporting Support
Write optimal SQL queries for data exploration, ad-hoc analysis, and troubleshooting.
Support the creation of reports, dashboards, and self-service analytics assets in collaboration with data analysts and business teams.
Translate business questions into data requirements and deliver actionable insights or datasets.
Operational Support & Data Deliveries
Monitor data pipelines and data delivery processes to ensure SLAs for timeliness, freshness, and accuracy are consistently met.
Proactively identify, troubleshoot, and resolve data issues impacting downstream consumers or business operations.
Manage incidents related to data availability and quality; participate in on-call rotations as needed.
Implement data quality checks, observability, and alerting to maintain high reliability of data deliveries.
Automate operational tasks and continuously improve data delivery processes.
Collaboration & Best Practices
Work cross-functionally with analysts, data scientists, engineers, and business stakeholders to understand data needs and deliver solutions.
Document data pipelines, models, lineage, and processes.
Contribute to data governance, security, and best practices across the data platform.
Requirements
5+ years of professional experience in data engineering or a closely related role (analytics engineering experience is highly relevant).
Strong proficiency in SQL and Python.
Hands-on experience building and maintaining data pipelines and working with cloud data platforms/warehouses (Snowflake, BigQuery, Redshift, Databricks, etc.).
Experience with data orchestration tools (Apache Airflow, Dagster, Prefect, or similar).
Solid understanding of data modeling techniques and dimensional modeling.
Experience performing data analysis and working with BI/visualization tools (Looker, Tableau, Power BI, or similar).
Proven ability to troubleshoot data issues and support operational reliability/SLAs.
Strong communication skills and ability to collaborate with both technical and non-technical stakeholders.
Bonus Points
Experience with dbt for data transformation and modeling.
Knowledge of data observability/monitoring tools.
Experience with real-time/streaming data technologies (Kafka, Flink, etc.).
Familiarity with CI/CD practices for data pipelines.
Experience in data quality frameworks and governance.
Bachelor’s degree in Computer Science, Engineering, or a related quantitative field (or equivalent practical experience).
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Troveo
View Company ProfileTroveo (operating under troveo.ai) is the premier, enterprise-grade AI training data platform, digital rights management pioneer, and ethically sourced dataset marketplace engineered to function as the definitive, high-velocity multi-modal data licensing, curation, and transformation layer for leading frontier AI laboratories and machine learning developers globally. Founded by creator-economy and digital media innovator Marty Pesis, the company completely eliminates the severe systemic friction of modern AI alignment and training—where foundation model developers confront legal barriers, data scarcity, and regulatory pushback over scraped web materials, while content creators, filmmakers, and broadcast networks miss out on monetization loops for their expansive non-public archives—by deploying an advanced, rights-cleared data intelligence matrix. Moving far beyond traditional stock media brokers or rigid, unannotated data lakes, Troveo natively unifies automated multi-channel video ingestion, advanced machine learning annotation (indexing clips across hundreds of semantic and action-aligned dimensions), secure private-to-lab licensing infrastructure, and a transparent creator royalty tracking dashboard into a single high-availability data engine workspace. As of mid-2026, the company manages the world's largest licensed repository of real, non-public data, boasting over 10 million hours of video, 5 million hours of high-fidelity audio, and hundreds of thousands of specialized clips optimized for robotics and action-aligned spatial computing. Serving over 7,000 independent and enterprise content owners—including iconic pop-culture and broadcasting giants like Barstool Sports, Sinclair Broadcast Group, and Nine Network—the infrastructure processes high-concurrency data engineering pipelines and millions of dollars in creator payouts with production-hardened systems precision. Under the hood, its technology core utilizes sophisticated algorithmic labeling pipelines, rigorous digital rights validation workflows, and secure delivery infrastructure designed to accelerate model development while strictly enforcing zero public scrapability. What sets Troveo apart is its uncompromising dedication to replacing legally fragile web-scraping habits with absolute intellectual property alignment, ethical data industrialization, and training pipeline velocity; by bridging the gap between performance-intensive structural AI optimization and a fair, creator-first licensing model, the enterprise remains the definitive cornerstone of modern algorithmic data engine infrastructure and global AI model transformation.
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