Hire Data Analyst — Numbers You Can Actually Trust
When you hire a data analyst, you should get decisions-grade numbers — not vanity dashboards nobody opens. I build tested SQL and dbt pipelines, define each KPI once so every team measures the same thing, and run cohort analysis that reveals what’s really driving retention. Every metric ships with its definition and owner.
I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. I embed with your team, ship the analytics myself, and hand over documented models — need predictions? See hire data scientist, or browse all hiring.
Analytics your whole company can trust
SQL & dbt pipelines
Version-controlled dbt models with tests on every critical column, so broken upstream data fails loudly in CI instead of silently corrupting the dashboards your executives check each morning.
KPI definitions & metric layer
One agreed definition per KPI, documented and enforced in the model layer — ending the monthly ritual of three teams reporting three different revenue numbers to leadership.
Cohort & retention analysis
Signup-cohort retention curves and driver analysis that separate real product improvements from mix shifts and seasonality, so leadership invests quarter after quarter in what actually moves the needle.
Executive dashboards
Dashboards designed around decisions, not data dumps — each chart answers one question, loads fast, and links back to the dbt model that produced it for traceability.
Funnel & conversion analytics
Step-by-step funnel instrumentation with drop-off analysis by segment and device, so marketing and product see exactly where users leak and which fixes to ship first this quarter.
Self-serve analytics setup
Semantic layer and governed datasets your team can query safely without writing SQL, plus training so analysts stop being the bottleneck for every ad-hoc question asked.
From messy data to trusted metrics
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Metrics audit
I map your existing reports, find conflicting KPI definitions, and trace each number back to its source tables — usually surfacing silent breakage within days.
Pipeline build
dbt models with tests, documentation, and CI checks go live incrementally — finance and product see trusted numbers in weeks, not quarters.
Dashboard rollout
Dashboards ship one decision at a time, each reviewed with its stakeholders — adoption is designed in, not hoped for after launch.
Enable & handover
Your team learns to extend the models and dashboards themselves, with runbooks for common breakages — the analytics keeps working after I leave.
Why hire through a Fractional CTO
Dashboards fail when nobody owns the definition behind the number. As a Fractional CTO, I treat analytics as infrastructure: tested models, documented KPIs, and pipelines your engineers can extend — the same engineering discipline behind $25M+ in client revenue enabled across 100+ projects.
You get 15+ years of data work across 6 countries and ex-teams like Phaedra Solutions and Integriti — senior judgment that keeps your metrics honest without a full-time hire.
Data analyst FAQs
Data analyst vs data scientist — which do I need?
Need to know what happened and why? Hire the analyst. Need to predict what happens next — churn, demand, risk? Hire the scientist. Many teams need the analyst first to fix their data foundations.
What tools do you work with?
SQL, dbt, and modern warehouses like BigQuery and Snowflake, plus BI tools your team already uses. I standardize on boring, proven tooling — exotic stacks are how analytics projects go to die.
How fast can we get trustworthy dashboards?
Core KPIs usually land in 2-3 weeks once warehouse access is sorted. The honest answer depends on your data quality — the metrics audit in week one tells us exactly where we stand.
Can you fix our conflicting KPI numbers?
Yes — that’s the most common engagement. I trace each conflicting number to its query, get stakeholders to agree on one definition, and enforce it in the dbt model layer permanently.
Do you work with our in-house analysts?
Happily — I usually pair with them, review their models, and level up their dbt and SQL practices. The goal is a team that doesn’t need me after handover.
Hire a data analyst who defines done
Tell me which numbers your team argues about, and I’ll make them trustworthy — tested pipelines, agreed KPIs, dashboards that get used. Dubai-based, working worldwide.