Hire Data Analytics Consultant
who speaks business, not just SQL

With the decisions, not the data. We define the 8–12 metrics that run the business, then trace backward to sources. Most companies find 70% of what they need already exists — it's just disconnected and untrusted.

For most growth-stage companies: BigQuery as warehouse, Looker Studio for dashboards — low cost, low maintenance. I'll recommend based on your team's skills and scale, not tool fashion.

Working core dashboards typically land in 4–6 weeks. Full pipeline maturity — with monitoring and documentation — in about a quarter. You'll see credible numbers early; perfection comes later.

Yes — tracking repair is often the first engagement. Broken events poison everything downstream, so I audit and fix measurement before building anything on top of it.

Happily — the best engagements pair my architecture experience with someone who knows your business data. I also mentor junior analysts into owning the system afterward.

It depends on scope, not headcount. I typically work three ways: a fixed-scope sprint for a defined build such as a warehouse and core model, a monthly fractional retainer when you want ongoing ownership and improvement, or a short day-rate engagement for an audit or second opinion. Fixed scope suits a clear deliverable; a retainer suits a system that must keep evolving. I quote after a short discovery call so the number reflects your actual data sources and state, not a generic rate card.

A full-time analyst is right once you have a stable system and steady question volume, but they are expensive to hire before the foundation exists and often lack warehouse engineering depth. Agencies bring people but frequently leave you with a black-box setup you cannot maintain. When you hire a data analytics consultant like me, you get senior architecture and analysis in one person, built to be handed over. I often build the system, then help you recruit the analyst who will run it.

Both. I default to BigQuery plus dbt for growth-stage teams because it is cheap to start and low-maintenance, but I work across Snowflake, Redshift and Postgres, and dbt is my standard transformation tool regardless of warehouse. For ingestion I use Fivetran or Airbyte for standard connectors and custom Python where a source has none. The warehouse matters less than modelling it well, so I pick based on your existing stack, team skills and cost profile rather than pushing one platform.

Both, but I am deliberate about sequencing. Forecasting, churn scoring and lifetime-value models only pay off on clean, well-modelled data, so I rarely lead with them. Once the warehouse is trustworthy I build forecasts and propensity models, usually starting with interpretable approaches in SQL or Python before anything heavier, and I push the outputs back into your tools so they drive action. If someone offers you machine learning before fixing the data foundation, be sceptical.

Yes. Many clients keep me on a light fractional retainer for pipeline monitoring, new metric requests and quarterly model reviews, especially while an in-house analyst ramps up. But my goal is that you could leave at any point, because the system is documented, tested and owned by your team rather than locked in my head. I would rather be retained because the work keeps adding value than because you cannot run the thing without me.

Your data stays in your own cloud accounts and warehouse, under your control, not mine; I work inside your environment with scoped access. I design for privacy from the start: limiting PII in analytics tables, applying column-level controls, respecting consent in event tracking, and keeping data residency aligned with GDPR or UAE PDPL where they apply. Compliance is far easier when it is built into the model rather than bolted on after an audit flags it.