Independent Data Consultant — Montréal

End-to-end
expertise across
the entire
data stack.

From raw pipelines to production-grade analytics and strategy — I help scale-ups build data functions that are reliable, scalable, and genuinely useful to the business.

About

A generalist who has
seen every layer

With over a decade working across every discipline in data, I bring a perspective that most specialists can't — I can build it, analyse it, and explain why it matters to the business.

My background is a blend of mathematics and computer engineering, with graduate degrees from a leading technical university. That combination gave me a deep quantitative foundation and a bias for building things that actually run in production.

What makes me different is that I don't have a specialisation silo. Most data professionals are strong on either the engineering or the analytics side. I am fluent in both — I can design systems that are architecturally sound and analytically meaningful at the same time, and speak the language of every stakeholder in the room.

Credentials

MSc in Mathematics (finance & insurance track)
BSc in Mathematics
BSc in Computer Engineering

Career Arc

01 Data Analyst Global financial services & insurance Quantitative foundations across complex data environments at scale.
02 Data Scientist Mobility startup Modelling and experimentation in a fast-paced product environment.
03 Data Scientist → Data Engineer Digital health scale-up — startup through IPO Rode 10× hypergrowth in three months, through an IPO and acquisition.
04 Staff Data Engineer & Team Lead Data platform ownership Platform ownership, hiring & coaching, exec-level stakeholder management.
05 Staff Software Engineer & Tech Lead Platform engineering Data contracts at scale, cross-functional technical leadership.
01
Full data lifecycle fluency
From source system ingestion and pipeline engineering to data modelling, analytics, experimentation, and executive reporting.
02
Proven at every stage of maturity
Built data functions from zero, scaled them through rapid growth, and matured them into trusted, org-wide platforms. I know where the traps are at each phase.
03
Bridges engineering and the business
Architecture with engineers. ROI with your C-suite. In the same day. That rare combination prevents the misalignment that kills most data initiatives.
04
Academic depth, practical output
Rigorous by training, pragmatic by experience. The kind of background that shows up in the quality of the work, not just the resume.

Services

How I can help

I engage as a hands-on consultant — embedded in your problem, not just advising from a distance.

01

Data Strategy & Roadmap

Assess where your data function stands today, identify the biggest leverage points, and define a realistic roadmap to get to the next stage of maturity — whether you're just starting out or trying to scale.

02

Data Engineering & Platform

Design and build reliable data pipelines, warehouse architecture, and data platforms. From ingestion to transformation to delivery — using dbt, Airflow, Snowflake, and modern open-source tooling.

03

Analytics & Reporting

Turn raw data into decision-ready insights. Define the metrics that matter, build centralised reporting cockpits, and establish a single source of truth your stakeholders can trust.

Coming soon
04

Data Science & Experimentation

Predictive modelling, A/B testing frameworks, and experimentation infrastructure that turns hypotheses into decisions.

Coming soon
05

Data Quality & Governance

Data contracts, observability, masking, and quality frameworks that prevent trust from eroding at scale.

Coming soon
06

Team Building & Mentorship

Hiring, structuring, and growing data teams. Coaching individual contributors and helping data leaders level up.

Tech stack

Languages & Stores
Python SQL Terraform Snowflake PostgreSQL S3
Tools & Frameworks
dbt Airflow Meltano Singer FastAPI Snowplow
Analytics & BI
Tableau Metabase A/B Testing Predictive Analytics
Domains
Healthcare FinServ & Insurance Mobility & IoT SaaS

Writing

On data, teams,
and getting it right

All posts →
Strategy

Why data maturity is a people problem, not a tooling problem

Most organisations invest in platforms, not processes. The result is expensive infrastructure and dashboards nobody trusts.

Coming soon →
Team Building

The trap of premature specialisation in early-stage data teams

Hiring a data engineer before you have a data problem, or a data scientist before you have clean data — these are expensive mistakes.

Coming soon →
Engineering

Build vs. buy: a framework for data tooling decisions that won't date badly

The modern data stack grows faster than any team can evaluate it. Here's how to make tooling decisions you won't regret in eighteen months.

Coming soon →

FAQ

Common questions

The questions I get most often before a first call — engagement model, pricing, remote work, project length, and fit.

How do you typically engage with clients?

As an independent consultant on a retainer or a fixed-scope project. I embed in your team for the duration — joining stand-ups, code reviews, and stakeholder meetings — rather than advising from a distance. Engagements start with a short paid scoping conversation so we both confirm the work is the right fit before committing.

How do you price your work?

Day rates for retainers, fixed quotes for well-defined projects. Rates depend on scope, duration, allocation, and whether the work is remote, hybrid, or on-site. After the first scoping call you receive a written proposal with a clear price and deliverables — no surprises.

Do you work remotely, and where are you based?

I'm based in Montréal and work remotely with clients across North America and Europe — with a particular focus on Germany and Munich, where I studied and previously worked. On-site visits at kick-off or for key workshops can be arranged when they add real value.

How long does a typical engagement run?

Most engagements run three to nine months at part-time or full-time allocation. Shorter focused audits (two to four weeks) are also available for clients who want a structured assessment of their data function before committing to a larger initiative.

Who do you usually work with?

Primarily early- and mid-stage startups, scale-ups, and SMEs that have outgrown ad-hoc analytics and need to put real data foundations in place. I also take on focused modernisation projects with established companies in financial services, insurance, healthcare, and mobility.

Contact

Have a data
problem worth
solving?

Whether you have a well-defined project or just a nagging data problem with no clear solution yet — I'm happy to have a no-commitment conversation to see if I can help.

info@laulou-data.com