Job description

About Us Todyl puts world-class networking and security within reach of every business. The Todyl Security Platform converges SASE, SIEM, Endpoint Security, GRC, MXDR, and more into a cloud-native, single-agent platform built for MSPs, MSSPs, and mid-market IT professionals. We are on a mission to protect small and medium-sized businesses from ever-changing cyber threats, and we are a fast-paced team that cares deeply about carrying out that mission together. About the Role The Data team at Todyl owns the systems that turn high-volume security telemetry into something the rest of the platform and our partners can act on. We design and operate the ingestion pipelines, storage, and search and analytics layer that power detection, investigation, and the business intelligence our AI/ML and product teams build on. Our work is judged by how reliably and quickly data lands, stays correct, and stays queryable at scale, not by how many tickets we close. Our platform ingests terabytes of security telemetry a day through a Kafka-compatible streaming layer into Quickwit and ClickHouse. This is a data infrastructure role. If your data experience is mostly an application database behind a product, this role is probably not the right fit. This is a senior individual contributor role on a small team. You will work with our Staff data engineer, who sets the technical direction for the team. You will own significant parts of our ingestion and storage systems end to end, become the go-to expert in your area of the platform, and take projects from an ambiguous problem statement to a system that runs quietly in production. The team is small, so what you build matters immediately and visibly. You will have real influence over how we solve problems, and room to grow into broader technical scope as the team grows. In this role, we are looking for someone who: • Is recognized as the go-to expert in their technical domain, designs complex features with minimal rework, and debugs the hardest issues their team faces. • Has a strong sense of ownership and stays with the work through stabilization, not just through a successful deploy. They own what they build through its full lifecycle and don't leave it at 90 percent or abandon it after the initial delivery. • Makes sound architecture decisions for multi-sprint work, evaluates several approaches before choosing a direction, and explicitly balances technical debt against delivery speed with clear reasoning. • Designs for failure modes and graceful degradation, anticipates significant scaling needs, and builds monitoring, alerting, and debugging tools into their designs rather than adding them later. • Anticipates problems before they happen and proposes solutions proactively, thinking roughly 6 months ahead. • Mentors other engineers with real, visible improvement in their skills, and leads thorough design reviews. • Communicates technical concepts clearly to both engineers and non-technical stakeholders, and quantifies the business impact of their technical work. • Actively uses AI tooling in their day-to-day work. • Thrives in a fast-paced startup environment and is comfortable adapting existing systems to fast-changing requirements. What you will do: • You will design and implement data ingestion pipelines that pull from many sources through Kafka-compatible streaming into Quickwit and ClickHouse for real-time analytics, search, and visualization, optimized for scalability, reliability, and fault tolerance under high volume and velocity. • You will own complex features and projects end to end, from an ambiguous problem statement through to long-term maintenance, breaking that ambiguity into achievable milestones and estimating large epics with reasonable accuracy. • You will design and tune the storage layer: index and table design, partitioning, retention, and query performance, weighing trade-offs across cost, scale, security, and maintainability, and documenting designs the team will reference long after they ship. • You will build monitoring, alerting, and debugging into what you design so we catch correctness and freshness issues early, and you will measurably reduce how long it takes us to detect and resolve them. • You will participate in the team's on-call rotation, resolve most issues independently, and follow up on incidents and the toil they expose rather than leaving that work unfinished. • You will partner with the AI/ML and product teams at the design phase of the systems you own rather than reviewing them the week before launch, and serve fast, correct queries to the teams that build on our data. • You will mentor engineers on the team through pairing, design partnership, and code review, and lead thorough design reviews. • You will proactively reduce technical debt across multiple projects a year and take initiative on important work nobody has assigned. • You will treat code review and testing as quality levers, not checkboxes. You design inherently testable systems with comprehensive coverage, catch testing gaps in other people's designs, and watch dashboards and logs to verify your own changes after they ship. • You will run retrospectives with genuine follow-through, document the patterns you find, and build onboarding material that makes the next person faster. Important note: We expect the person in this role to actively use AI tools, including tools like Claude, to accelerate development, reduce toil, and solve data engineering problems more quickly. We will talk about how you use AI in your work during the interview, and we expect your fluency with these tools to grow as part of your professional development here. Required: • 5+ years of software engineering, with 2+ years building and operating data infrastructure in production • Production experience with a streaming platform: Kafka, Redpanda, Kinesis or a similar log-based system. You have built or operated producers and consumers, and you have dealt with consumer lag. • Production experience with at least one search or analytics store: ClickHouse, Elasticsearch, OpenSearch or Quickwit. You have designed indexes or tables and tuned queries that were too slow. • Strong Go or Python, and strong SQL • Demonstrated experience owning complex data features or projects end to end, through to production and long-term maintenance • Experience with high data volume, and the ability to explain the cost and performance trade-offs you made Nice to have: • Quickwit specifically • Columnar formats and object storage (Parquet, Iceberg, S3) • Log shippers such as Vector or Fluent Bit • Observability for data systems (Grafana, Prometheus) • Multi-tenant SaaS, AWS, and Linux • Security telemetry or SIEM experience What We Offer For full-time employees, Todyl offers comprehensive benefits including: • Medical, dental, and vision coverage • Health savings and flexible spending accounts (HSA/FSA) • Life insurance • Short- and long-term disability • Access to on-demand healthcare and telehealth services • Employee Assistance Program (EAP) • Flexible PTO in addition to 13 company holidays • 401(k) • Generous parental leave programs This role is based in Denver, CO, with 3 days per week in our office. The salary range for this position is $170,000-$190,000, plus equity. The actual annual salary for this role will depend on each candidate's experience, qualifications, and work location, with most new hires placed near the midpoint of the posted range to ensure fairness and consistency across our team. Todyl provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, transgender status, gender identity or expression, national origin, age, disability, marital status, genetic information, military status, or any other status protected by applicable federal, state, or local laws. We encourage you to apply even if you don't meet all the requirements listed. We are looking for the best person for the job, someone who brings a unique combination of skills and experience that makes them exceptional, even if they don't check every box.

Requirements

Must-haves

In this role, we are looking for someone who:
Is recognized as the go-to expert in their technical domain, designs complex features with minimal rework, and debugs the hardest issues their team faces.
Makes sound architecture decisions for multi-sprint work, evaluates several approaches before choosing a direction, and explicitly balances technical debt against delivery speed with clear reasoning.
Designs for failure modes and graceful degradation, anticipates significant scaling needs, and builds monitoring, alerting, and debugging tools into their designs rather than adding them later.
Anticipates problems before they happen and proposes solutions proactively, thinking roughly 6 months ahead.
Mentors other engineers with real, visible improvement in their skills, and leads thorough design reviews.
Communicates technical concepts clearly to both engineers and non-technical stakeholders, and quantifies the business impact of their technical work.
Actively uses AI tooling in their day-to-day work.
Thrives in a fast-paced startup environment and is comfortable adapting existing systems to fast-changing requirements.
You will build monitoring, alerting, and debugging into what you design so we catch correctness and freshness issues early, and you will measurably reduce how long it takes us to detect and resolve them.
You will participate in the team's on-call rotation, resolve most issues independently, and follow up on incidents and the toil they expose rather than leaving that work unfinished.
You will partner with the AI/ML and product teams at the design phase of the systems you own rather than reviewing them the week before launch, and serve fast, correct queries to the teams that build on our data.
You will mentor engineers on the team through pairing, design partnership, and code review, and lead thorough design reviews.
You will proactively reduce technical debt across multiple projects a year and take initiative on important work nobody has assigned.
You will run retrospectives with genuine follow-through, document the patterns you find, and build onboarding material that makes the next person faster.
5+ years of software engineering, with 2+ years building and operating data infrastructure in production
Production experience with a streaming platform: Kafka, Redpanda, Kinesis or a similar log-based system. You have built or operated producers and consumers, and you have dealt with consumer lag.
Production experience with at least one search or analytics store: ClickHouse, Elasticsearch, OpenSearch or Quickwit. You have designed indexes or tables and tuned queries that were too slow.

Nice-to-haves

Quickwit specifically
Columnar formats and object storage (Parquet, Iceberg, S3)
Log shippers such as Vector or Fluent Bit
Observability for data systems (Grafana, Prometheus)
Multi-tenant SaaS, AWS, and Linux
Security telemetry or SIEM experience

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

restaimlgopythonsqlaws

Platform: rippling
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave

Professional Development