Senior Software Engineer, Distributed Systems — Core Data Infrastructure

Software Engineer, Distributed Systems — Core Data Infrastructure

Remote in the US or Canada · Full-time

$180K–$320K base + competitive equity

Visa sponsorship available

The company

This is a data infrastructure company building the activation layer on top of the modern data warehouse.

Hundreds of companies, including Autotrader, Calendly, Cars.com, Monday.com, and PetSmart, use the platform to move customer data from their warehouse into the tools that drive marketing, personalization, and business operations.

The company pioneered the Composable Customer Data Platform category and is now extending the product into AI Decisioning, where marketers define goals and guardrails and AI agents personalize customer interactions at the individual level.

The company is 350 people, founded in 2018, and has raised $320M. Its Series C valued the business at $1.2B. Investors include Sapphire Ventures, Amplify Partners, ICONIQ Growth, Bain Capital Ventures, Y Combinator, and Afore Capital.

The founders were early employees at Segment, where they saw both the impact and the limitations of the first generation of CDPs.

The role

You will work on the distributed systems that power the company’s core syncing engine.

This engine moves large volumes of customer data from warehouses into downstream destinations that businesses rely on every day. The work sits directly on the path between customer data infrastructure and production business workflows.

This is not a backend product role with distributed-systems flavor. It is a systems role focused on performance, reliability, throughput, customer scale, and infrastructure design across multi-cloud and multi-region environments.

You will own projects end to end, work directly with customers on difficult scaling problems, and influence the technical direction of a core platform used by other engineering teams.

The technical problem

The simple version is moving data from one place to another.

The real problem is doing it quickly, reliably, and repeatedly across large customer datasets, heterogeneous destinations, strict rate limits, warehouse constraints, multi-cloud deployments, and global data residency requirements.

Customers want to sync more data, more often, into business-critical destinations such as Facebook and Snapchat. Every part of the syncing path becomes a possible bottleneck: query planning, batching, scheduling, retries, destination APIs, caching, backpressure, failure recovery, and observability.

The next phase adds another constraint: today, the system is primarily batch-based. The long-term direction includes real-time and streaming syncs from sources such as webhooks and queues.

There is also a low-latency product surface: the company’s Personalization API provides a caching layer on top of data warehouses for real-time personalization use cases, with sub-30ms p90 response times and QPS in the millions.

This role is for someone who wants to reason from first principles about distributed systems under real customer pressure.

What you’ll own

• Sync performance: find and remove bottlenecks across the syncing engine so customers can move large datasets faster and more predictably.

• Distributed execution: design systems that handle scheduling, batching, retries, rate limits, backpressure, and partial failures without hiding complexity behind fragile abstractions.

• Streaming architecture: help evolve the platform from batch-only syncs toward real-time syncs from webhooks, queues, and other streaming sources.

• Scalability and reliability: architect for the next order of magnitude of growth across throughput, latency, infrastructure cost, and operational load.

• Low-latency serving: contribute to infrastructure behind the Personalization API, including caching, query paths, tail latency, and high-QPS production behavior.

• Multi-region and multi-cloud infrastructure: support and extend deployments across regions and cloud providers, including new regions required for customer data residency.

• Customer-scale debugging: work directly with customers to understand their hardest scaling issues and turn those issues into durable platform improvements.

• Technical direction: influence what the team builds, how systems are designed, and where engineering time should be spent.

Who this is for

You are likely a strong fit if you have:

• 7+ years of software engineering experience, with meaningful work on distributed systems, infrastructure, or high-scale backend systems.

• Strong general engineering judgment and the ability to reason clearly about latency, throughput, consistency, retries, failure modes, and operational tradeoffs.

• Experience building systems where correctness and reliability matter under customer load.

• Comfort debugging performance across multiple layers of a system, from application logic to queues, databases, caches, cloud infrastructure, and third-party APIs.

• Experience owning projects from ambiguous problem statement to production rollout.

• The ability to work directly with customers without turning engineering into reactive support.

• Strong communication skills when tradeoffs are subtle and the right answer depends on workload shape, constraints, and failure tolerance.

• A bias toward simple systems that expose the right abstractions rather than complex systems that only work when everything goes right.

Top candidates will be able to explain where a distributed system fails, what evidence they would collect first, and which tradeoffs they would refuse to hide.

Relevant technical areas

You do not need experience with every item here, but the strongest candidates will have depth in several:

• Distributed systems

• High-scale backend infrastructure

• Data movement and data pipelines

• Job scheduling and orchestration

• Streaming systems, queues, or event-driven architectures

• Caching and low-latency serving

• Multi-region or multi-cloud infrastructure

• Warehouse-adjacent systems

• Reliability engineering and observability

• Performance optimization under real production constraints

Good judgment matters more than familiarity with a specific vendor or framework.

Why now

The company already has meaningful scale: hundreds of customers, major enterprise usage, and infrastructure that sits directly in the path of customer operations.

The next constraint is not adding more product surface area. It is making the core data movement layer faster, more reliable, more real-time, and more globally deployable.

That creates a high-leverage opening for a senior distributed-systems engineer: the right person will influence the architecture behind the company’s core platform, not just optimize isolated services.

This role is not for you if

• You want a role focused mainly on product CRUD or frontend-adjacent backend work.

• You prefer systems where scale problems are already solved and the architecture is mostly fixed.

• You do not enjoy debugging messy production behavior across multiple infrastructure layers.

• You want to stay far from customers and only receive fully specified technical tasks.

• You are uncomfortable owning reliability, performance, and operational consequences after launch.

• You prefer clever abstractions over boring systems that work under load.

Compensation and logistics

• Base salary: $180K–$320K

• Equity: competitive

• Location: remote within the US or Canada

• Employment: full-time

• Visa support: available for most visas

Interview process

• Recruiter screen: 30 minutes

• Hiring manager interview: 30 minutes

• System design screen: 45 minutes

• System design interview: 90 minutes

• Final call

About Aurora

Aurora helps exceptional engineers find the right role at some of the most ambitious startups worldwide.

• We work with teams that value high ownership, strong technical standards, and clear impact.