Pull messy source data from APIs, reports, email attachments, Sheets, and ad platforms into repeatable ingestion workflows.
Flow
APIs / Email / SheetsParserBigQueryChecks
Related
Stack
Meta APIGmail APIBigQueryCloud Run
Marketing Data Engineering & Automation
Build data systemsthat scale withyour ambition.
I build API integrations, BigQuery/dbt models, Google Sheets automations, and custom dashboards that turn messy reporting workflows into trusted analytics systems.
Proof points
- 30+
- client workflows shipped
- 10+
- source types connected
- Raw → Gold
- reporting layers built
Upwork, Fiverr, and direct product work across data, automation, and reporting.
Ad platforms, ecommerce reports, affiliate networks, Sheets, email, APIs, and warehouses.
Messy inputs turned into validated, dashboard-ready and AI-ready datasets.
Systems connected
Sources, warehouses, and workflows.
Compact connectors for the tools teams already use, shaped into a quieter data system.
Meta AdsGoogle AdsLinkedIn AdsBing AdsGA4Amazon SellerShopifyWooCommerceAwinRakutenTradeTrackerGmailGoogle SheetsBigQuerydbtDataformAirflowSupabaseGeminiMySQLPostgreSQLApps ScriptAWSGCPMeta AdsGoogle AdsLinkedIn AdsBing AdsGA4Amazon SellerShopifyWooCommerceAwinRakutenTradeTrackerGmailGoogle SheetsBigQuerydbtDataformAirflowSupabaseGeminiMySQLPostgreSQLApps ScriptAWSGCP
Data workflow
Across the full
data workflow.
From messy source pulls to trusted reporting layers, each service maps to a practical part of the journey: ingest, model, automate, validate, report, and govern.
30+ builds shipped across APIs, warehouses, automations, and dashboards.
View selected workPull messy source data from APIs, reports, email attachments, Sheets, and ad platforms into repeatable ingestion workflows.
Flow
APIs / Email / SheetsParserBigQueryChecks
Related
Stack
Meta APIGmail APIBigQueryCloud Run
Turn raw data into clean, dashboard-ready models with controlled grain, metric definitions, and reconciliation checks.
Flow
RawSilverGoldMarts
Related
Stack
BigQueryDataformdbt-style SQLLooker-ready
Replace manual exports, copy-paste reporting, and recurring spreadsheet work with controlled scripts and scheduled automations.
Flow
TriggerScriptSheet outputLogs
Related
Stack
Apps ScriptSheets APIPythonGitHub Actions
Add row counts, duplicate checks, platform total reconciliation, freshness checks, and run logs so reporting does not become guesswork.
Flow
TotalsDatesDuplicatesAlerts
Related
Stack
AssertionsReconciliationFreshnessLogs
Build clean reporting surfaces in Looker Studio, Google Sheets, or custom dashboards once the underlying data is reliable.
Flow
MartsDashboardFiltersDecisions
Related
Stack
Looker StudioSheetsDashboardsBI exports
Create structured signals, tags, rules, and benchmark layers that can power recommendations, alerts, and AI-assisted analysis.
Flow
MediaTagsClustersRulesInsights
Related
Stack
GeminiEmbeddingsRule EngineBenchmarks
Client feedback
What clients
called out.
Marketplace feedback from completed data engineering, dashboard, API, and automation projects.
7
Committed to Quality
5
Clear Communicator
4
Collaborative
3
Reliable
2
Accountable for Outcomes
2
Professional
1
Solution Oriented
The pattern is usually the same: less manual checking, cleaner reporting, and systems that keep running after handoff.
View Upwork profileVerified marketplace reviewUpwork5.0
Ahmad is an exceptional engineer. He solved really hard problems that required a lot of iteration and testing, and was super helpful with the hand-off to our internal team.
Reviewed work
DV360, ADH, BigQuery and SQS data pipeline
Solution OrientedClear CommunicatorDetail Oriented
Review
Looker Studio dashboards connected to API data
CollaborativeCommitted to Quality
Ahmed is very competent and easy to work with. He has great communication and technical skills. He went above and beyond to deliver excellent work. I highly recommend him.
5.0
Upwork
Review
Data aggregation using Google BigQuery
ReliableAccountable for Outcomes
Ahmad was great to work with. Always prompt and did everything that was asked of him. Readily available and trustworthy.
5.0
Upwork
Review
GA4 and BigQuery specialist support
Clear CommunicatorReliable
Great guy with extensive knowledge in BigQuery and Data Looker. Has a remarkable ability to tackle complex tasks and go above and beyond to deliver great results.
5.0
Upwork
Workflow transformation
From manual reports
to trusted systems.
Before
Ad hoc reporting
- 01Manual exports from each platform
- 02Spreadsheet cleanup and copy-paste joins
- 03Inconsistent dashboard numbers
- 04Late reports and unclear ownership
After
Reliable data operations
- 01Automated ingestion with scheduled checks
- 02Validated warehouse layers and audit trails
- 03Trusted dashboards using shared definitions
- 04Alerts before stakeholders notice failures
How I work
One process,
clear checkpoints.
See the plan, the tradeoffs, and the system taking shape at every step.
Map the existing workflow, data sources, failure points, and business decisions the system needs to support.
Define storage, orchestration, validation, access, and ownership boundaries so the data flow is maintainable.
Build connectors, models, tests, and reporting layers with documentation and stakeholder visibility throughout.
Tune performance, cost, monitoring, and handoff paths so the system keeps working cleanly after delivery.
Discover
Trace the workflow before touching the architecture.
Map the existing workflow, data sources, failure points, and business decisions the system needs to support.
Discover
Trace the workflow before touching the architecture.
Map the existing workflow, data sources, failure points, and business decisions the system needs to support.
Insights
Thoughts on data
engineering.
Marketing Data
Why Marketing Dashboards Break When Spend Is Modeled at the Wrong Grain
A practical note on campaign, ad, creative, order, and CRM grain problems in marketing warehouses, and how I model them before they reach dashboards.
May 20265 min read
AI-ready Analytics
What a Creative and Campaign Intelligence Data Platform Needs Before AI
A field note on the ingestion, warehouse, validation, and prompt-traceability layers I look for before adding AI analysis to campaign and creative data.
May 20265 min read
Automation
When Google Sheets Automation Is Enough, and When You Need a Warehouse
How I decide whether a reporting workflow should stay in Google Sheets and Apps Script or move into BigQuery, a database, or a custom dashboard.
May 20265 min read
FAQ
Questions before
the first build.
A good data project starts with the shape of the workflow, not a tool choice. These are the questions teams usually ask before I map the first system.
First step
Bring one painful report, the sources behind it, and the decision it is supposed to support.
No. Dashboards are usually the final surface. The core work is ingestion, validation, modeling, orchestration, and making sure the numbers are dependable before they reach a chart.
Yes. Most useful systems start with imperfect exports, inconsistent naming, duplicated records, or missing ownership. The first step is mapping what exists and turning it into a reliable flow.
Yes. Recurring attachments, shared spreadsheets, and operational files can be parsed, validated, loaded, and monitored so teams stop moving numbers by hand.
Yes. I build connectors for marketing platforms, commerce tools, internal systems, and other APIs where off-the-shelf integrations are too limited or too fragile.
By adding validation rules, reconciliation checks, lineage, alerting, clear definitions, and warehouse layers that separate raw inputs from modeled reporting tables.
I start with a short discovery pass: data sources, current reporting pain, required decisions, failure points, and the smallest useful system that can be shipped first.
Ready to start?
Let's build your
data foundation.
Book a focused discovery call to outline the current workflow, the desired reporting layer, and the smallest reliable system worth building first.