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SalesLoft is building a Predictive Revenue System that combines AI and real signals to help enterprise revenue teams make confident decisions. The company serves thousands of customers daily and is transforming how modern revenue teams operate.
As a Senior Data Engineer, you will own the design, build, and maintenance of scalable data pipelines that move, transform, and secure data across the organization. You will build the data foundations powering security analytics, internal reporting, and trustworthy data flows the business depends on. This is a hands-on role with real ownership where you engineer pipelines that are reliable, well-governed, and built to scale.
Day-to-day responsibilities include:
- Design, build, and maintain scalable data pipelines that reliably move and transform data across systems
- Model and structure data for analytics, reporting, and security use cases
- Ensure data quality, lineage, and governance across owned pipelines and datasets
- Build and maintain data infrastructure powering security analytics and internal decision-making
- Partner with security, analytics, and business teams to deliver trustworthy, well-governed data
- Automate and harden data workflows, treating reliability and security as first-class concerns
You will work within the Security and data organization, where data engineering, security, and analytics share ownership of reliable, well-governed data. The role emphasizes building durable infrastructure, fixing root causes of data quality issues, and respecting the security and privacy of customer data.
REQUIREMENTS:
- Strong production data pipeline experience building and maintaining pipelines at scale
- Proficiency in SQL and a major programming language (e.g., Python) for data engineering
- Experience with modern data pipeline and orchestration tooling and cloud data platforms
- Solid understanding of data modeling, data quality, and governance practices
- Experience with cloud infrastructure (AWS or GCP)
- Experience building data infrastructure for security analytics or other sensitive-data use cases
- Familiarity with streaming data systems (Kafka, Kinesis) and modern warehouse/lakehouse platforms
- Exposure to data infrastructure supporting AI/ML workloads
