Sr. BI & Data Analytics Consultant
10+ years building the analytics that marketing, finance, operations and clinical teams rely on: from Azure SQL pipelines and Salesforce automation to executive dashboards and spend forecasts.
About
I work across Marketing, Finance, Operations, Contact Center and clinical research, turning business questions into data models, and data models into decisions.
Over a decade I have moved from healthcare data migrations, to e-commerce BI, to end-to-end analytics consulting for a multi-location healthcare group. The common thread: measurable outcomes, cross-functional leadership, and dashboards people trust.
Selected work
Each case study opens a full interactive report with dashboards, filters and methodology.
E-commerce · Forecasting
A month-end forecast built from just a few days of order data, with device-level scenarios and an adjustable growth rate.
Healthcare · Ad spend & revenue
Year-over-year spend, collections and return across practice locations, with quadrant analysis to guide budget shifts.
Healthcare · Campaign & funnel
Three years of campaign cost and lead-funnel data, sliced by practice, market, media type, lead type and vendor.
Technical toolkit
Track record
Plan · Act · Learn
Perspective
The automation below already runs on rules I built by hand. Agentic AI is the same instinct taken further: a system that plans a task, checks its own work, and asks only when it's unsure – applied to the reporting and monitoring hours that still eat an analyst's week.
Four stages, on a loop – each run feeding the next.
“Tell me why ad spend missed target” – not the steps to get there.
Which tables to query, which report to update, who to notify.
Pulls data, drafts the narrative, flags anomalies, with the tools an analyst would use.
Compares the outcome to what happened and adjusts, with a human reviewing before anything ships.
An agent that watches the dashboards I already automate and drafts the executive summary and anomaly callouts before anyone opens the file – turning a reporting job into a five-minute review.
An agent sitting on a pipeline around the clock, catching a broken load or a stale metric before a stakeholder does, and opening a ticket with the root cause already attached.
Ask a question in ordinary language and get back a validated chart, not a support ticket – so the team spends less time waiting on an analyst for numbers that already exist.
Turn the rule-based reports, like the ad-spend one above, into agent-drafted narratives, with a human sign-off before anything goes out.
Pipeline monitoring and anomaly flags before any agent is allowed to change data or push a report on its own.
So a wrong call is as easy to trace and reverse as a human one, with the same audit trail as the data-quality checks I run today.
From monitoring, to drafting, to acting – one step at a time, and only once the step before it has held up.
This is where I see agentic AI adding the most value in BI: taking over the reporting and monitoring hours already being automated by rule, not replacing the judgment calls that still need a person.
Resume
Sr. BI & Data Analytics Consultant with 10+ years across BI, data engineering, Salesforce and marketing analytics.
Credentials
Also passed Microsoft Exam 761 (Querying Data with Transact-SQL) and Exam 762 (Developing SQL Databases), October 2020. Education: Board of Intermediate and Secondary Education, F.Sc. Pre-Medical, Lahore (2008–2010).
Contact
Based in Lahore, Pakistan and working remotely with US teams. Send a note and I will reply within a couple of working days.
