DevRev
Data Engineer
About this role
DevRev seeks a Senior Data Engineer to design and operate scalable data platforms that power AI-driven customer experiences and business intelligence. You'll own critical data architecture, lead cross-functional partnerships, and establish engineering excellence practices while mentoring the team.
What you'll do
- Own data architecture and design scalable pipelines for ingesting, transforming, and serving large data volumes
- Partner with Software Engineering, Data Science, and Product teams to translate complex data requirements into robust technical solutions
- Define and enforce data quality, reliability, SLA/SLO standards for systems under your ownership
- Design and optimize data models, databases, and SQL workloads for performance and storage efficiency
- Build reusable frameworks and tooling to streamline data product development across the organization
- Mentor engineers through design reviews and technical guidance while promoting engineering best practices
What they're looking for
- Production data pipeline and distributed systems design
- Programming in Go, Java, C++, Python, or JavaScript with strong SQL expertise
- Relational and NoSQL database architecture and optimization
- Cloud data infrastructure (BigQuery, Snowflake, or similar)
- Distributed data technologies (Kafka, Spark, Flink, Trino/Presto)
- Data modeling, query optimization, and indexing strategies
- Observability, monitoring, and data quality frameworks
- Technical mentorship and engineering leadership
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DevRev
DevRev builds an AI platform that enables enterprise customers to design and deploy intelligent agents, automations, and integrations to transform their business operations. The company is hiring Forward Deployed Engineers to architect and implement custom AI-driven solutions embedded with customers, as well as Enterprise Sales Engineers to guide customers through technical evaluation and adoption of its platform.
- Website
- devrev.ai
Likely interview questions
- Describe a large-scale data pipeline you designed—what were the key scalability and reliability challenges, and how did you address them?
- How do you approach data model design when balancing query performance, storage efficiency, and maintainability?