Data Engineer

Troveo builds a data platform for AI labs and model builders. The Data Engineer will own the analytics data lifecycle, building and maintaining pipelines and a scalable warehouse while supporting data modeling, analysis, quality assurance, and reliable delivery to downstream teams.

Responsibilities:

• 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
• 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
• 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
• 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
• 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)