B2B data for AI agents and GTM tools. 350M+ contacts. Unmetered access.
Data Engineer
Location
United States
Posted
146 days ago
Salary
0
Seniority
Senior
Job Description
Data Engineer
RevenueBase
• Build and maintain production-ready data pipelines using DBT, Snowflake, and modern orchestration tools. • Own data engineering features end-to-end, from implementation through optimization and deployment. • Fix and improve existing pipelines - identify bottlenecks, resolve issues, and enhance performance. • Drive automation initiatives across the data stack to accelerate delivery and reduce manual interventions. • Provide 2nd line support for B2B customers - investigate data issues, clarify edge cases, and ensure customers can trust their data. • Design and implement new data import pipelines as we expand our data source coverage. • Implement data quality improvements - validation, monitoring, and testing to ensure reliable, accurate data delivery. • Contribute to code reviews, architectural discussions, and data engineering best practices.
Job Requirements
- 3+ years of professional data engineering experience
- Strong fundamentals in SQL, data modeling, Python and ETL/ELT principles
- Must have: DBT - hands-on experience building and maintaining transformation pipelines
- Nice to have: Snowflake
- Databricks
- AWS (S3, Lambda, Glue, etc.)
- Prefect or similar orchestration tools (Airflow, Dagster)
- Solid understanding of data quality principles, testing strategies, and monitoring practices.
- Comfortable working in a fast-moving, remote-first environment.
Benefits
- Competitive compensation based on experience
- Meaningful ownership and long-term growth opportunities
- Flexible working hours
- Fully remote-friendly team
- Direct collaboration with founders and core engineering leadership.
Related Guides
Related Categories
Related Job Pages
More Data Engineer Jobs
• Collaborate with team members and stakeholders to understand data requirements and contribute to building scalable data pipelines and workflows in Databricks. • Develop and implement ETL/ELT processes using Databricks, Python, SQL, and related tools to ingest, transform, and prepare data. • Assist in optimizing data workflows for better performance, reliability, and cost-efficiency within Databricks environments. • Support the creation and maintenance of data models, tables, and integrations in cloud platforms (Azure, AWS, or similar). • Work closely with cross-functional teams (data analysts, scientists, and engineers) to deliver clean, accessible data for analytics and reporting. • Monitor data pipelines, troubleshoot basic issues, and contribute to documentation and best practices. • Stay curious about new Databricks features and data engineering trends to support ongoing improvements.
• Kierowanie budową i rozwojem nowych potoków danych w środowisku RTB. • Projektowanie systemów o ultra-niskiej latencji (miliony QPS), w tym budżetowanie latencji oraz optymalizacja wydajności. • Leadership technologiczny: podejmowanie decyzji architektonicznych, mentoring zespołu, wypracowywanie kompromisów technicznych pomiędzy jakością, skalowalnością i time-to-market. • Udział w implementacji rozwiązań w Pythonie, SQL i Kafka; analiza kodu w Go w serwisach partnerskich; praca ze stosem Scala/JVM (Spark/Flink). • Zarządzanie procesem wdrożeń oraz utrzymaniem systemów w środowisku chmurowym (głównie GCP).
• Повна відповідальність за аналітичний шар даних у Google Cloud на етапі активного масштабування компанії та реальний вплив на розвиток продуктів; • Можливість відігравати ключову роль у побудові та розвитку аналітичної інфраструктури; • Робота з сучасним технологічним стеком (BigQuery, dbt, Airflow) і участь у формуванні технічних стандартів; • Тісна співпраця з продуктовими командами та прямий вплив рішень на продуктову, маркетингову й фінансову ефективність бізнесу.
Senior Data Engineer
RohirrimThe AI-Native Platform Rewriting the Architecture of Modern Acquisitions.
• Design, build, and optimize data pipelines and infrastructure for AI products • Collaborate closely with AI/ML teams, product teams, and security/compliance partners • Develop and operate ETL/ELT workflows • Implement and optimize vector database systems and embeddings pipelines • Architect and manage Azure-based data infrastructure • Build internal tools for metadata extraction and document parsing • Monitor and improve pipeline performance and reliability
