Location
This will be a fully remote position from within Mexico ONLY. Applicants must have work authorization to work from within Mexico.

Who we're hiring and why
At Spekit, we’re building the next generation of AI systems that transform how people learn and work – and that starts with a powerful technical foundation.
As we scale our AI platform, we’re tackling increasingly complex challenges in data performance, modeling, and system design thinking. We’re looking for a Senior Full Stack Engineer with a specialization in database and systems performance to help us level up our architecture, strengthen performance across the stack, and mentor others in building fast, maintainable systems.
This role sits on a team focused on accelerating execution and delivering high-impact product experiences. We’re looking for someone who is a strong software engineer first, someone who brings clarity and systems thinking to every feature you design. With database and performance depth as their superpower, they’ll instinctively optimize queries, model data effectively, and design systems that scale gracefully. This Sr. Software Engineer will help us evolve how data flows across our platform, powering intelligent enablement at scale.
The Opportunity
This is a hybrid role: part full-stack engineer, part database systems expert. You’ll contribute across the stack while owning the optimization and scalability of our data layer, ensuring our growing AI platform remains fast, reliable, and beautifully engineered.
You’ll work closely with other engineers to design data models, optimize queries, and shape system boundaries that balance product velocity with long-term technical health. And as a senior member of the team, you’ll mentor others and influence architectural decisions that shape Spekit’s future.
Must-Haves

Product-based mindset: We think long-term with the product vision in mind. We empathize with the customer - whether that customer is an end user, another team, a 3rd party integrator, or Spekit's own employees. You have a desire to develop a product that end users LOVE to use on a day to day basis!

Proficient in Python/DjangoDatabase depth as a superpower: proven expertise in query optimization, indexing strategies, schema design, and performance tuning (PostgreSQL preferred)

Systems thinking: ability to reason about domain boundaries, dependencies, and long-term architecture within a monorepo context

Mentorship ability: communicates trade-offs clearly (technical, product, and economic), helps elevate team skills, and guides less experienced engineers in database best practices

Experience with distributed/asynchronous systems that improve reliability and scale (e.g., messaging queues, background tasks, observability)

A data engineering or integration background — valuable, but not required for success

A collaborative mindset that helps the team design better data flows, make thoughtful architectural tradeoffs, and write more performant code

Nice-to-Haves

Full-stack software development experience: able to self-serve on end-to-end features (React, Rust, Django/Python)

Experience with distributed/asynchronous systems: working knowledge of messaging queues (RabbitMQ, Dramatiq), background task orchestration, and observability

Data modeling across systems: ability to design abstractions that unify heterogeneous external data models

Cloud-native familiarity: exposure to Redis, Kubernetes, AWS (especially RDS/managed Postgres)

Background in CRM integrations, particularly working with APIs from platforms like Salesforce or HubSpot

How You’ll Apply These Skills

Integration & data ingestion: experience extracting data from third-party APIs, designing ETL-like pipelines, and handling rate limits, retries, and deduplication

CRM API expertise: experience working with CRMs and APIs designed for platforms like Salesforce, HubSpot, and similar systems

Prior staff-level or architecture influence: experience making technical decisions that shaped domain boundaries or scaling strategies

What Success Looks Like

30 Days

Ramp up on Spekit’s codebase, deployment pipelines, and existing database structures.

Ship at least one small end-to-end feature to demonstrate full-stack proficiency.

Pair with teammates to review queries and identify “low-hanging fruit” optimizations.

60 Days

Propose and implement at least one schema or index improvement that meaningfully reduces query cost.

Contribute to discussions on system boundaries and technical debt areas in retros or planning.

Begin mentoring peers in query review and debugging workflows.

90 Days

Lead the design of a performance-sensitive feature, shaping both product and data flow decisions.

Deliver at least one integration or data ingestion improvement (e.g., Salesforce API interaction, background task orchestration).

Be recognized by the team as a go-to partner for both feature delivery and performance questions.

About Spekit
Spekit is an AI enablement platform that helps revenue teams learn and perform faster — in the flow of work. Used by thousands of sales and success teams at world-class companies, Spekit delivers the knowledge employees need, exactly when and where they need it.
We’re growing fast, we’re product-obsessed, and we’re just getting started

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