Senior Software Engineer – REDAPL Graph Engine

Remote from: Spain, Germany +1 more, France
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 2 Jun 2026
Apply before: 2 Jul 2026
Experience level: Senior
Views / Applies: 31 / 10

We are building the monitoring and security platform for developers, IT ops teams and business users in the cloud age.

Computer Software
2010

AI Summary

Datadog is seeking a Senior Software Engineer to advance its REDAPL Graph Engine, a platform that tracks infrastructure relationships and ingests over 4.5 million updates per second. The role involves building efficient query engines, leading high-impact projects, and collaborating with product teams. Candidates should have extensive backend experience, distributed systems expertise, and familiarity with graph databases or query engines. Datadog offers competitive benefits and a hybrid workplace.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires deep expertise in distributed systems, graph engines, and cross-team leadership, placing it at a high difficulty level. However, the company supports growth and collaboration, avoiding the highest difficulty.

Salary Analysis

AI Insight The offered salary is not specified, but for a Senior Software Engineer at Datadog, the median is estimated at $200,000 based on market data. This is competitive for top-tier tech companies, aligning with industry standards for experienced engineers in distributed systems.

Key Skills

Distributed Systems Graph Databases Query Engines Backend Engineering Scalability Performance Optimization Apache Iceberg Cloud Infrastructure Platform Engineering Technical Leadership

Cover Letter Sample

I am excited to apply for the Senior Software Engineer position at Datadog, where I can leverage my expertise in distributed systems and query engines to advance the REDAPL Graph Engine. With a strong background in building scalable platforms and leading technical initiatives, I am drawn to Datadog's mission of providing observability and security for modern infrastructure.

In my previous role, I designed and optimized a graph-based data platform handling millions of updates per second, focusing on performance and resilience. I have experience with graph databases and query planning, and I am eager to contribute to REDAPL's next-generation capabilities.

Datadog's hybrid culture and emphasis on collaboration align with my work style, and I am motivated to drive high-impact projects that enhance customer insights. I look forward to discussing how my skills can support Datadog's growth.

Possible Interview Questions

Describe a time you designed a distributed system that handled high throughput. What challenges did you face and how did you address them?

I worked on a data ingestion pipeline processing 5 million events per second. Challenges included data consistency and latency. We implemented partitioning, asynchronous processing, and a consensus algorithm to ensure reliability.

How would you approach building a graph query engine that supports efficient relationship traversals at scale?

I would start by understanding the query patterns and data model, then use an adjacency list or property graph model. For scalability, I'd implement indexing strategies, caching, and parallel traversal. I'd also consider using existing frameworks like Apache Calcite for query planning.

Explain a situation where you had to lead a technical initiative with cross-team dependencies. How did you ensure alignment and success?

I led a project to migrate a legacy data store to a new graph database. I organized regular syncs with product and infrastructure teams, defined clear milestones, and used shared documentation. We resolved conflicts by prioritizing critical features and iterating.

What experience do you have with graph databases like Neo4j or Amazon Neptune? Can you compare their strengths?

I have used Neo4j for its Cypher query language and strong ACID compliance, and Amazon Neptune for its managed service and integration with AWS. Neo4j is great for complex traversals, while Neptune offers scalability with less operational overhead.

How do you balance performance, cost efficiency, and operability when designing a platform?

I start with clear requirements and SLAs. For performance, I use benchmarks and profiling. Cost efficiency comes from resource optimization (e.g., auto-scaling, data compression). Operability is ensured through monitoring, alerting, and gradual rollouts. Trade-offs are documented and reviewed.

We are looking for a Senior Software Engineer to help us take REDAPL, our Referential Data Platform, to the next level. REDAPL is Datadog’s main platform for tracking our customers’ infrastructure resources and relationships. The platform enables products where customers can understand, keep track of, and gain insights into their infrastructure related to performance, cost, security, and more.

Many Datadog products use REDAPL today such Cloud Security Posture Management, Resource Catalog, Cloud Cost Management, and Service Catalog and others – REDAPL ingests more than 4.5mil updates/second.

As a Senior Engineer, you will drive, lead and collaborate on projects both inside and outside the platform. You can expect to contribute to key technical decisions relating to our data ingestion, processing, and query pipelines.

At Datadog, we place value in our office culture – the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

Build a query engine that supports efficient relationship traversals for our most demanding workloads.
Contribute to design and drive high-priority, high-visibility projects to increase the platform’s value, resilience, and scalability across multiple teams.
Lead and guide other engineers through architectural platform decisions
Identify potential system risks and trends in reliability and design solutions to address them
Provide input on prioritizing engineering-led initiatives in short- and long-term planning and roadmaps
Collaborate with internal product teams to understand their requirements and how we plan for their product growth as they integrate and depend on REDAPL

Who You Are:

You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience
You have worked extensively with multiple types of data stores.
You have contributed to initiatives in a platform or infrastructure-focused environment for several years
You have led impactful technical initiatives in an environment where performance, cost efficiency, ease of use, and resilience/operability are first-order concerns
You have significant back-end engineering experience and have built, and operated distributed systems to solve problems at a high scale

Bonus points:

You have experience working with Graph Databases such as Neo4j, Memgraph, JanusGraph, ArangoDB, Amazon Neptune or PuppyGraph.
You have production-grade experience with Apache Iceberg
You have a solid grasp of query planning or better yet – contributed to a production-grade query engines or related projects (e.g. Trino, Calcite, Substrait, DataFusion, etc) ideally demonstrated through open-source contributions or PMC membership on projects.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development.
Have a lot of influence on product direction and impact on the business .
Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn
Competitive global benefits
Continuous professional development

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

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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 impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

Equal Opportunity at Datadog:

Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

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