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Multigres Engineer

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  • Julkaistu 31.08.26 01:04

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Opportunity details

About This Role.

AI Summary

Supabase is seeking a senior-level distributed systems engineer to build Multigres, its open-source horizontally scalable Postgres system. The role focuses on query routing for sharded databases, consensus and replication, materialization pipelines, change data capture, and connection-pooling infrastructure. Success requires deep expertise in Postgres internals, distributed transactions, consensus algorithms, stream processing, observability, and low-latency networking. The engineer will work asynchronously with Postgres, networking, infrastructure, and open-source community contributors. This is a fully remote full-time role with substantial ownership over foundational database infrastructure.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5

EasyHard

Pace & Pressure

4/5

RelaxedFast-paced

Autonomy Level

5/5

GuidedFull ownership

Communication Load

4/5

IndependentCollaborative

AI insightThis is a highly specialized systems role involving correctness-critical distributed database components, including consensus, replication, sharding, and transactions. It demands strong theoretical knowledge alongside practical performance, reliability, and operability engineering experience.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate

$190,000

US market range$160k–$230k

0$253k

AI insightNo actual salary is disclosed in the posting; these are estimated US annual base-salary market figures for a senior distributed database/software engineer. Compensation may vary materially by location, experience with Postgres internals and consensus systems, and equity structure.

Core skills

Skills And Capabilities Most Closely Associated With This Opportunity.

Distributed systemsPostgreSQLDatabase shardingConsensus protocolsRaftReplicationDistributed transactionsChange data captureStream processingNetwork optimization

Sample interview questions

How would you design query routing for a sharded Postgres deployment?

I would define a clear shard-key strategy, maintain a strongly consistent metadata catalog, and route single-shard queries directly to the owning shard. For multi-shard queries, I would explicitly manage planning, fan-out, aggregation, failure handling, and transaction semantics while minimizing cross-shard operations through schema and routing design.

What trade-offs would you evaluate when selecting a consensus protocol for distributed database metadata or replication?

I would assess fault tolerance, leader-election behavior, operational maturity, latency, membership changes, storage guarantees, and implementation complexity. Raft is often attractive for its understandable leader-based model and mature ecosystem, while the final choice should reflect the required consistency scope and performance characteristics.

How do you approach correctness in a distributed transaction system?

I start by defining the isolation guarantees and failure model, then make ordering, idempotency, retries, and durable state transitions explicit. I use invariants, deterministic and fault-injection tests, observability around transaction lifecycles, and careful recovery procedures to validate behavior under partial failures and network partitions.

How would you build a reliable change data capture and materialization pipeline?

I would capture ordered source changes with durable offsets or LSNs, ensure consumers are idempotent, and persist checkpoints only after successful application. The pipeline should support schema evolution, replay, backpressure, monitoring for lag, and reconciliation mechanisms to detect or repair divergence.

What metrics and debugging tools are most important for low-latency distributed database infrastructure?

I would instrument end-to-end and per-stage latency percentiles, throughput, queue depth, replication lag, lock waits, connection-pool saturation, error rates, and resource utilization. Distributed tracing, structured logs with request and shard identifiers, profiling, and targeted load or fault tests are essential for isolating bottlenecks and failure modes.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.

Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth.

We’re looking for an engineer to help build the future of distributed Postgres. You’ll work on Multigres, our open-source distributed database system that brings horizontal scaling to Postgres.

In this role, you’ll architect and implement critical distributed database infrastructure including sharding, consensus protocols, and materialization systems. You’ll collaborate closely with our Postgres, networking, and infrastructure teams to push the boundaries of what’s possible with distributed databases.

What You’ll Be Responsible For

Design and implement query routing logic for sharded databasesBuild consensus and replication systems to support distribute durabilityDevelop materialization pipelines for migrations and change data captureContribute to connection pooling infrastructure and intelligent workload isolationCollaborate with the open-source community on Multigres development

You Might Be a Good Fit If You Have

This role requires deep technical expertise in distributed databases and systems. For detailed qualifications, see our contributor qualifications document.

Key Areas Of Expertise

Database sharding, relational algebra, and Postgres internalsConsensus protocols (Raft, Paxos, FlexPaxos) and distributed transactionsStream processing, materialization, and change data captureBuilding robust, performant distributed systems with strong observabilityLow-latency infrastructure and network protocol optimization.

What We Offer

Fully Remote

We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world.

ESOP

Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together.

Tech Allowance

Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work.

Health Benefits

Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us.

Annual Off-Sites

Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year.

Flexible Work

We operate asynchronously and trust you to manage your own time. You know what needs to be done and when.

Professional Development

Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth.

About The Team

Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love.

~400 team members60+ countries20+ languages spokenOver $1B raised (including our $500M Series F)540,000+ community members

We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.

Apply now >

Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›

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Job Complexity

5/5

EasyHard

Pace & Pressure

4/5

RelaxedFast-paced

Autonomy Level

5/5

GuidedFull ownership

Communication Load

4/5

IndependentCollaborative

Estimated job medianMarket rate

$190,000US market range$160k–$230kHow would you design query routing for a sharded Postgres deployment?

I would define a clear shard-key strategy, maintain a strongly consistent metadata catalog, and route single-shard queries directly to the owning shard. For multi-shard queries, I would explicitly manage planning, fan-out, aggregation, failure handling, and transaction semantics while minimizing cross-shard operations through schema and routing design.

What trade-offs would you evaluate when selecting a consensus protocol for distributed database metadata or replication?

I would assess fault tolerance, leader-election behavior, operational maturity, latency, membership changes, storage guarantees, and implementation complexity. Raft is often attractive for its understandable leader-based model and mature ecosystem, while the final choice should reflect the required consistency scope and performance characteristics.

How do you approach correctness in a distributed transaction system?

I start by defining the isolation guarantees and failure model, then make ordering, idempotency, retries, and durable state transitions explicit. I use invariants, deterministic and fault-injection tests, observability around transaction lifecycles, and careful recovery procedures to validate behavior under partial failures and network partitions.

How would you build a reliable change data capture and materialization pipeline?

I would capture ordered source changes with durable offsets or LSNs, ensure consumers are idempotent, and persist checkpoints only after successful application. The pipeline should support schema evolution, replay, backpressure, monitoring for lag, and reconciliation mechanisms to detect or repair divergence.

What metrics and debugging tools are most important for low-latency distributed database infrastructure?

I would instrument end-to-end and per-stage latency percentiles, throughput, queue depth, replication lag, lock waits, connection-pool saturation, error rates, and resource utilization. Distributed tracing, structured logs with request and shard identifiers, profiling, and targeted load or fault tests are essential for isolating bottlenecks and failure modes.

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