Sifflet implements data quality checks ranging from simple (detecting null values) to very complex (time series forecasting models to validate that the distribution of a set of columns hasn’t changed in an unexpected way, taking into account seasonality, one-off events…). This foundation powers many features, such as automatically merging related alerts into incidents.

The monitoring team is responsible for evolving the data quality checks performed by Sifflet, and all associated workflows. As a member of this team, you will:

• Design and implement new types of data quality checks.

• Build features to allow users to efficiently monitor their entire data stack, such as automated monitor suggestions.

• Design advanced solutions to cut the alerting noise, such as automated incident root cause analysis.

• Scale our monitoring engine to support more and more customers. Some customers require monitoring massive data sets.

Some projects you could be working on

• Add support for automated data profiling (understanding the expected distribution of values in all columns), and build an automated anomaly detection on top of that.

• Implement automated monitoring at scale, without requiring the user to manually define its own quality checks.

• Design new, more powerful data quality checks.

Our stack

• The monitoring engine is built with Python 3 and its large data/ML ecosystem (notably PyTorch).

• Jobs are orchestrated with Temporal.

• The web API is written in (modern) Java with Spring Boot 3, the web frontend is a VueJS application written in Typescript. You may occasionally need to make minor changes to this code base.

• Infrastructure: Kubernetes (AWS EKS clusters), MySQL (on AWS RDS).

• Plus a few supporting services: Gitlab CI, Prometheus/Loki/Grafana, Sentry…

While not directly part of our stack, expect to gain a lot of knowledge on many products in the modern data ecosystem. The subtleties of BigQuery or Snowflake will soon be very familiar to you.

Preferred qualifications

• +7 years of experience in a backend engineer role or equivalent.

• General knowledge of the “modern data stack” ecosystem, especially data warehouses and databases. You don’t have to know everything upfront of course, you’ll pick up what you need on the job.

• Willingness to learn Python if you don’t already know the language. While this is not a machine learning (ML) engineering position, you will also be exposed to ML topics.

• You value ownership of your projects from design to production, and aren’t afraid of taking initiatives.

None of the people who joined Sifflet perfectly matched the described requirements for the role. If you’re interested in this position but don’t tick all the boxes above, feel free to apply anyway!

• Introduction Call (30min) – A conversation with a team lead to discuss your background, the role, and what excites you about Sifflet.

• Technical Interviews – Two in-depth assessments:

  ◦ Coding Interview (90min) – Evaluate your problem-solving and coding skills.

  ◦ System Design Interview (90min) – Assess your ability to design scalable and efficient systems.

• Meet the Product team (30min) – Gain insights into our vision, challenges, and ambitions.

• Meet the team(30min)– Meet your future colleagues, experience our culture, and see firsthand what makes our team awesome!

• Reference Call – A final step to gather feedback from previous colleagues or managers.

Why you should join us?

• We offer a competitive salary along with meaningful company equity.

• You’ll have the opportunity to actively contribute to building the team in India while working directly with the Paris team.

• You’ll work alongside real experts across many domains — there’s always someone to learn from. We also run regular tech talks where the team shares cool projects and new technologies.

• You’ll get deep exposure to the modern data ecosystem, quickly building strong expertise in data engineering, the modern data stack, and how data is actually used in real companies.

• Our culture is strongly team-oriented, focused on shipping things that work and bringing projects all the way to production.

• We’re building a genuinely great product, and just as importantly, a team people actually enjoy working with.