Arslan Zaheer
London,City of Westminster
Summary
Data professional and educator currently working as a data and systems consultant, with experience building scalable data systems and AI-driven applications in a SaaS environment. Skilled in building production-level pipelines and data-related products using Python, SQL, and AWS architecture, with a focus on improving system performance for meaningful insights. Bring over three years of experience mentoring & coaching data professionals across Level 3,4 and 5 data standards, supporting strong learner outcomes and real-world application of analytics in their day-to-day roles.
Overview
1
1
Certification
7
7
years of post-secondary education
5
5
years of professional experience
Work history
Data Consultant
MootDesign
London
2025.05 - 2026.06
- Performance engineering: led a project to improve multi-tenant business reporting for 300+ clients. Previously, reporting ran entirely on the main transactional database (AWS RDS - PostgreSQL), causing significant slowdowns during reporting spikes as the platform grew. By offloading the analytical workload onto a read replica and applying targeted indexing, partitioning, and raw SQL optimisation over the Django ORM, dashboard load times dropped from around 450ms to roughly 125ms. Heavy operational reports — such as job- and visit-level analytics for the largest tenants — went from over 2.3 seconds to around 0.6 seconds, a 70–75% reduction in end-to-end latency and roughly a 3× improvement in report responsiveness — turning reporting from a retrospective review into a real-time decision tool.
- Refactored core Django models and data architecture: alongside the performance work, refactored the data model into an account-centric design —introduced a CustomerAccount junction entity to resolve a many-to-many relationship we had with 500+ customers using multiple businesses, and re-pointed jobs and visits at CustomerAccount ID instead of carrying both customer and business IDs. Each customer-at-a-business is now a single, distinguishable account, giving clients clean, roll-up metrics at any level — job, visit, customer, or business. Currently extending our architecture to dimensional modelling to future-proof reporting, as the platform expands into new industries and integrates multiple data sources.
- Internal dashboard: built internal ETL pipelines using SQL (via pg8000) and Pandas and delivered a centralised internal dashboard providing data quality metrics and holistic operational visibility.
- AI and ML application: developed an end-to-end information-delivery solution for business clients. Built a Selenium web scraper that pulls business data and feeds it into a Mistral LLM RAG pipeline to auto-generate business content. Content is admin-reviewed for quality and then stored in a business-specific AWS S3 bucket, with strict tenant isolation in PostgreSQL.Information is retrieved from S3 and delivered via an NLP-powered chatbot embedded as a widget on each business's website, providing end customers with instant, accurate answers about the business.
- Enhanced customer communication process: refactored backend messaging modules to centralise comms from Twilio (SMS), SendGrid (Email), and Chatbot streams into a single, cohesive pipeline for the business dashboard.
Data Analyst and Data Engineering Mentor
CambridgeSpark
London
2023.02 - 2025.04
- Supported Level 4 (Data Analyst) and Level 5 (Data Engineering) apprenticeship programs — guiding learners through the full data analytics and engineering life cycle, from ingestion and data modelling through analysis, and deployment on cloud platforms.
- Provided tailored support to learners including topics like OLTP/OLAP databases and warehousing, database schema and modelling, natural language processing, predictive analysis, and cloud computing concepts using python libraries like pandas, scikit-learn, and nltk etc.
- Fostered a collaborative learning environment by conducting monthly one-on-one sessions and group discussions to go through complex technical topics.
- Achieved a cohort distinction rate above 90% — through tailored technical coaching, rigorous portfolio feedback, and structured project guidance across machine learning applications including regression, classification, and time series forecasting models.
- Worked closely with Operations and Internal Quality Assurance and delivered a 60% timely completion rate in an 18 month accelerated L4 program, contributing to the overall quality and consistency of the cohort.
Data Business Coach
JustIT
London
2022.09 - 2023.02
- Guided apprentices in the Data Technician and Data Analyst apprenticeship programs and guide them to implement the newly attained knowledge and skills of Excel and Python.
- Collaborated with apprentices and employers to evaluate taught content and identify opportunities for skill application, developing actionable plans that align training with workplace needs.
Data Mentor
Decoded
London
2021.10 - 2022.08
- Managed a cohort of 40+ apprentices and was responsible for the full Data Analyst (Level 4) apprenticeship journey.
- Supported learners with technical queries and programming concepts including Python & SQL programming, data visualisation using Power BI and Tableau, and predictive analysis.
- Brainstormed and helped learners identify opportunities within their workplace to embed work-based learning and taught techniques to create real impact.
- Provided detailed feedback on the portfolios of data analytical projects to ensure they meet the required Data Analyst Level 4 standards.
Education
MSc - Business Intelligence and Digital Marketing
Brunel University
London, United Kingdom 2020.09 - 2021.09
MSc - Big Data Science
Queen Mary University of London
London, United Kingdom 2018.09 - 2020.12
BSc - Telecommunication and Networking
COMSATS University
Islamabad 2014.01 - 2018.01
Skills
- IDEs: JupyterLab, PyCharm, Cursor and Colab
- Languages: Python, SQL
- Databases: PostgreSQL, SQLite, MS SQL - SQLAlchemy, Django ORM
- Cloud: AWS (ECS, S3, RDS)
- Data Engineering: ETL pipelines, Dimensional modelling (star schema), Multi-tenant data architecture
- Artificial Intelligence / Machine Learning: Large Language Models (Mistral RAG), Natural Language Processing, Predictive Analysis - NLTK, Scikit-learn, Statsmodels, TensorFlow, and Prophet
- Visualisation & BI: Power BI, Python Dash
- Integrations and other Libraries: Selenium, Twilio, SendGrid, Pandas, Seaborn, Bokeh, Plotly
Certification
- IBM Data Science/Big Data (2018)
- DevOps Bootcamp (2025)
- Artificial Intelligence Bootcamp (In progress)
Timeline
Data Consultant
MootDesign
2025.05 - 2026.06
Data Analyst and Data Engineering Mentor
CambridgeSpark
2023.02 - 2025.04
Data Business Coach
JustIT
2022.09 - 2023.02
Data Mentor
Decoded
2021.10 - 2022.08
MSc - Business Intelligence and Digital Marketing
Brunel University
2020.09 - 2021.09
MSc - Big Data Science
Queen Mary University of London
2018.09 - 2020.12
BSc - Telecommunication and Networking
COMSATS University
2014.01 - 2018.01