This role exists because we need an engineer capable of working on structural, cross-functional topics with high impact — someone who builds foundations, not just features.

1. Design and evolve our critical data components

You will be at the heart of Supervizor’s data architecture:

• Design and maintain robust, scalable transformation pipelines

• Evolve our layered architecture on BigQuery (ingestion, transformation, exposure)

• Define and enforce data quality and modeling standards

• Contribute to shared tooling and data contracts between teams

2. Contribute to the execution engine at the core of the product

SQL controls are Supervizor’s core product capability. This area is actively being structured:

• Contribute to the evolution of the execution engine (architecture, performance, robustness)

• Design mechanisms for corrections management and impact assessment at scale across the entire client base

• Work alongside domain experts to understand the limits of the current model and shape what controls will look like tomorrow

3. Be the Python reference for the organization

You are not confined to a single team — you bring your expertise across all teams:

• Define and maintain Python conventions (style, tooling, patterns)

• Lead cross-functional code reviews across all projects

• Contribute to developer experience and platform observability (Datadog, logging best practices, alerting)

You may come from software editing, data preparation, workflow execution, or somewhere else entirely. What matters: you know how to build robust systems that process, transform, and expose data at scale.

Must-haves:

• 10 to 15 years of software engineering experience, ideally in a B2B SaaS environment

• Python as your primary language — you are comfortable designing, challenging, and reviewing code in depth

• You have already designed or contributed to a processing engine, runtime, transformation system, or critical pipeline

• Comfortable with GCP (Cloud Run, BigQuery, GCS)

• You appreciate well-designed systems, documentation that stands the test of time, and tooling that genuinely improves team workflows

Nice to haves:

• Experience with dbt, DuckDB, or Datalake architectures

• Familiarity with Golang

• Interest in AI / ML (no need to be an ML engineer, but able to evaluate and challenge approaches)

• Experience with Datadog or equivalent observability tooling

Stack

Python · BigQuery · GCP (Cloud Run, GCS) · dbt · DuckDB · Golang · Datadog · Datalake

1. Discussion with our CTO

2. Technical interview with a Staff Engineer

3. Final interview with our CEO