Data Engineer
About The Position
Troveo builds a data platform for AI labs and model builders, providing licensed data across video, audio, text, and business workflows. The Data Engineer will build and maintain a scalable analytics data warehouse, contribute to data modeling and analysis, and ensure reliable data delivery to downstream teams and systems. This hands-on role spans the analytics data lifecycle, including ingestion, transformation, modeling, quality assurance, and timely delivery, in partnership with software engineers and business stakeholders. The role includes engineering batch and streaming ELT/ETL pipelines; designing and implementing data models; building documented, analytics-ready transformation layers; supporting SQL analysis, reporting, and self-service analytics; monitoring and troubleshooting data delivery; implementing data quality checks and observability; and documenting pipelines, models, lineage, and processes. The position also contributes to data governance, security, and platform best practices. The role is remote-first and open to working from anywhere in the US, with occasional team offsites. Compensation is a base salary of $160,000–$175,000, depending on experience and location, plus a competitive equity package.
Requirements
- At least 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 or warehouses such as Snowflake, BigQuery, Redshift, or Databricks.
- Experience with data orchestration tools such as Apache Airflow, Dagster, Prefect, or similar.
- Solid understanding of data modeling techniques and dimensional modeling.
- Experience performing data analysis and working with BI or visualization tools such as Looker, Tableau, Power BI, or similar.
- Proven ability to troubleshoot data issues and support operational reliability and SLAs.
- Strong communication skills and ability to collaborate with technical and non-technical stakeholders.
Nice To Haves
- Experience with dbt for data transformation and modeling.
- Knowledge of data observability or monitoring tools.
- Experience with real-time or streaming data technologies such as Kafka or Flink.
- Familiarity with CI/CD practices for data pipelines.
- Experience with data quality frameworks and governance.
- Bachelor’s degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.
Responsibilities
- Design, build, and maintain batch and streaming ELT/ETL data pipelines, optimizing performance, reliability, scalability, and cost.
- Design and implement conceptual, logical, and physical data models, including dimensional models and star or snowflake schemas.
- Build and maintain transformation layers using modern tools to create clean, documented, analytics-ready datasets.
- Apply data modeling best practices, versioning, testing, and documentation to promote consistency and reusability.
- Write SQL queries for data exploration, ad-hoc analysis, and troubleshooting.
- Support reports, dashboards, and self-service analytics assets with data analysts and business teams.
- Translate business questions into data requirements and deliver actionable insights or datasets.
- Monitor pipelines and data delivery processes to meet timeliness, freshness, and accuracy SLAs.
- Identify, troubleshoot, and resolve data issues affecting downstream consumers or business operations.
- Manage incidents related to data availability and quality and participate in on-call rotations as needed.
- Implement data quality checks, observability, and alerting to maintain reliable data delivery.
- Automate operational tasks and continuously improve data delivery processes.
- Collaborate 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.
Benefits
- Medical, dental, and vision coverage, 100% employer-paid for employees.
- Unlimited PTO and paid holidays.
- Annual learning stipend and access to top conferences.
- Direct mentorship from experienced founders.
- Competitive equity package.
- Budget for equipment and software.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
