Senior Data Engineer - Revenue Data Platform
DatadogBoston, Massachusetts, USA; New York, New York, USADev Eng
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About this role
About Datadog:
We're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at high scale—trillions of data points per day—allowing for seamless collaboration and problem-solving among Dev, Ops and Security teams globally for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way.
The Team:
The Revenue Data Engineering Teams designs, builds and runs the data pipelines and helper systems to accurately and in a timely manner quantify our customers’ usage across all Datadog products. This team is at the leading edge of any new product we release.
The Revenue Data Processing team builds and operates the data pipelines that does billing, and cost attribution for all Datadog products. We process terabytes of data daily to power revenue-critical systems and are at the center of every new product launch at Datadog.
As a Senior Software Engineer, you will own meaningful parts of a large-scale, mission-critical processing platform — driving architectural improvements, building new billing capabilities, and maintaining the high reliability bar our downstream consumers depend on.
You Will:
• Design and build high-throughput data pipelines for billing and cost attribution
• Drive platform improvements — latency reduction, Spark optimization, sharding, and cross-datacenter reliability
• Own root-cause investigations on billing accuracy issues in collaboration with Finance and Product teams
• Contribute to new billing features
• Work across Python and Scala, with technologies including Spark, Airflow, Trino, and Apache Iceberg
• Participate in on-call rotation and maintain a high reliability bar for production systems
• Contribute to engineering standards and help grow the technical culture of the team
You Are:
• You have significant experience building and operating production data pipelines at scale using Spark and Airflow
• You are comfortable owning complex, cross-functional investigations and driving them to resolution
• You understand how to balance feature delivery with platform health and technical debt
• You have strong analytical instincts and are rigorous about correctness in data systems
• You work well in ambiguous environments and can drive alignment across stakeholder teams
• You care about code quality, maintainability, and operational excellence
Bonus Points:
• Have experience with Apache Iceberg or Lakehouse architectures
• Have worked on billing, metering, or financial data systems
• Have experience with low-latency data serving or real-time aggregation pipelines
To conform to US export control regulations, candidates should be eligible for any required authorizations from the US government. This job is available in various departments within our company; to conform to US export control regulations, some of these roles may require candidates to be eligible for any required authorizations from the US government.
#LI-Hybrid
Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.
The reasonably estimated yearly salary for this role at Datadog is:
$192,000 - $240,000 USD
About Datadog:
Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they…
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