Data Engineer

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 the reliable delivery of data 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 robust, scalable ELT/ETL data pipelines (batch and streaming) from various source systems into cloud data platforms and warehouses.
• Optimize pipelines for performance, cost, reliability, and scalability.
• 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

• 7+ 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.