Description

I’m a freelance Data Analyst who helps organizations turn data into actionable insights that drive smarter marketing decisions.

With a background in Marketing Intelligence, I developed a strong foundation in data analysis, modelling, and translating complex datasets into clear business value. What started as an interest in marketing research evolved into a specialization in marketing analytics, automation, and experimentation.

I work with tools such as SQL and Python, and build data solutions using platforms like Databricks, Google BigQuery, Microsoft Azure, and Google Cloud Platform (GCP). For visualization and reporting, I use Power BI, IBM Cognos, and Looker Studio to translate complex data into clear, actionable insights.

I also have hands-on experience with marketing automation platforms such as SAS 360, SAS Enterprise Guide, Selligent, IBM Unica, and Relay42. In addition, I work with web analytics tools including Google Analytics (GA4), Adobe Analytics, and Webtrekk, and have experience with A/B testing, tracking, and tagging implementations (web and app) to optimize campaigns and customer journeys.

After gaining experience in corporate environments, I chose to move into freelance consulting to work more closely with organizations and help them unlock the full potential of their data — from raw data to insights to action.

I enjoy working on challenging data problems, continuously learning, and bridging the gap between data and business.

If you’re looking to strengthen your marketing analytics, improve campaign performance, or make better data-driven decisions, feel free to connect.

“Ambition has no limits.”

— Shoaib Sahel

Languages

Dutch
Native or bilingual
English
Fluent
German
Conversational

Workplace preferences

Can work on-site

Amsterdam (up to 50km)

ING
Data analyst a.i.
July 2024 - Today (2 years)
Amsterdam, Netherlands
Data analyst Affluent (apr. 2025 – present) and Data analyst Business Journey teams (jan. 2026 – present):
- Developed: a fully automated IBM Cognos dashboard with insights into the Affluent segment, based on app and web data.
- Conducted: in‑depth customer behavior analyses across the customer base, supported by app and web data.

Data analyst Client Development team (apr. 2025 – jan. 2026):
- Built: a Power BI dashboard with insights into ING’s primary customer groups.
- Developed: KPI targets based on historical trends and data‑driven forecasts.
- Identified: commercial growth opportunities through audience analyses and segmentation.
- Designed & executed: churn campaigns to reduce customer attrition, including A/B tests to validate effectiveness.

Data analyst Current Accounts and Loyalty teams (jul. 2024 – mar. 2025):
- Developed: a cluster model in Python to support new product strategies within Current Accounts.
- Built: an IBM Cognos dashboard with insights into account types within the retail customer base.
- Executed: various campaigns in SAS360, including the annual pricing‑adjustment campaign.
- Analyzed: customer behavior to generate new insights and pricing recommendations.
- For Loyalty: developed new insights in Power BI and built customer journeys through email and web/app interactions for promotional campaigns.

Additional tasks:
- Guided: colleagues in prioritization, data storytelling, and stakeholder management.

Tools used at ING: SQL, SAS 360, IBM Cognos, Power BI, Python, Google Cloud Platform, Google BigQuery, Adobe Analytics, Jira, Confluence
SQL SAS Python Microsoft Power BI Google Cloud Platform (GCP)
Factfinders
Data analyst a.i.
September 2024 - March 2025 (6 months)
Data analyst for the Data Tech team at Mogelijk Vastgoedfinancieringen (assignment):
- Responsible for migrating existing Excel reports to automated Power BI dashboards and for developing dashboards for management, finance, and operations.

Tools used at Factfinders (Mogelijk): SQL, Power BI, Jira
KPN
Data analyst a.i.
March 2024 - July 2024 (4 months)
Amsterdam, Netherlands
Data analyst TV & Entertainment and Change teams:
- Developed: automated Power BI dashboards with insights into customer contact, aimed at reducing call volumes.
- Combined: multiple data sources to identify patterns in what customers contact about and why.
- Facilitated: workshops with teams to analyze common contact reasons and determine improvement opportunities.
- Guided: colleagues in prioritization, effectively communicating insights, and building high‑value dashboards.

Tools used at KPN: SQL, Teradata, Power BI, Jira, Confluence.