Syedor Rahman

Head of Data & AI       

Datasense Management Consultancies, Dubai, UAE                                                 Jun 2024 – present

• Derive a greenfield data strategy for an aviation operator to process and govern batch/real time data from multiple sources including IOT sensors, booking systems and crucial ground aviation equipment. Resulting architecture was cloud based, deployed via DEVOPS IAC CI/CD pipelines, 4 9’s SLA for Uptime, transparent for regulatory requirements and horizontally scalable for future sites across the GCC. 
• Review current data & AI enablement strategy for a financial services operator; provide 2nd opinion on technology and governance choices to address risk mitigation for such a substantial financial investment. Conduct workshops to provide a detailed summary outlining alignment to priorities and future projects, alternative technology choices with pros/cons and a transparent independent evaluation that scoped greater coverage of the available data technology on the market.

Outcomes included deduplication of initiatives across departments, 20% cost savings on compute by negotiating with multiple vendors with clearer monthly requirements, 10% reduction on data processing times and potential data monetisation opportunities. 

• Derive comprehensive data management & AI strategies and roadmaps to align with an organisation's overarching business objectives by working closely with business leaders/tech teams to understand needs and develop tailored data & AI solutions.
• Establish data governance policies and procedures for managing data throughout its lifecycle, ensuring its integrity, protecting data from unauthorized access, loss and proper usage. 
• Evaluate existing data processes, systems, and databases to identify gaps, duplication and inefficiencies, recommend best practices and automated AI driven solutions. 
• Advise on and implement processes for integrating data from various sources to create a unified data architecture for Structured/Semi-Structured and Unstructured data to enable a composable CDP, reports, dashboards, actionable insights and automated self-learning AI Solutions. 
• Oversee the implementation of new data systems and solutions, including data migration and testing. Provide expert guidance on data management processes and train them on new systems and tools.

Head of Data Engineering       

Exinity Group, Dubai, UAE                                                                                              Dec 2022 – Jun 2024

• Built enterprise data strategy & governance (policy, lineage, quality, security) for multi-region deployments; reduced time-to-insight by 50% and enabled group wide KPI visibility, performance and automated audit returns for the first time.
• Designed IAC/DevOps Lakehouse (batch + real-time) on Azure/Databricks targeting 99.99% uptime; cut pipeline MTTR by 60%.
• Working with high volume real time streaming data pipelines that are fed from MT4 and MT5 trading platforms. Trading activity data exposure spanned FX. Commodities, indices and equities, copy trading, PAMM, Affiliates and IB’s
• Introduce clearer end to end data lineage by making use of the Atlan data governance tool to achieve insight & KPI validation. 
• Develop the engineering team into building robust data pipelines to support ML models, AI agents and advanced analytics on a new central unified data Lakehouse platform by upskilling 8 engineers that were spread across the world.
• With this enhanced data infrastructure, reliable data accessibility was enabled to 6 additional departments. End to End lineage visibility was also possible for the first time to non-technical users. 
• Facilitated training and development for team members, fostering a culture of continuous learning. Adopted teams to triage data initiatives with business value realisation and ROI metrics to make prioritisation transparent to stakeholders.   
• Investigate and propose AI tools and models that could drive efficiencies and AI understanding within the business. 
• Lead on ITDATASECOPS initiatives to promote secure movement, resting and processing of PII data. 

Head of Data

Vanquis Bank, London, UK                                                                                          Jan 2022 – Dec 2022

• Led on an initiative to reduce Azure OPEX costs by 30% by introducing end to end linage on Collibra for all data pipelines to surface duplication and possible optimisations; put together tech debt business cases to decouple and split into central data products that departmental data teams consume via databricks delta sharing APIs.
• Redistribute 40 data professionals into 6 central cross functional sprint teams with a balance of business knowledge across cards, loans, risk and compliance resulting in a 20% uplift in productivity and improved morale.
• Optimised data processing workflows, resulting in increased efficiency and reduced turnaround times.
• Established data governance frameworks that improved data quality by 30% on average and compliance across the organisation.
• Collaborated with stakeholders to align data strategies with business objectives, ensuring data initiatives supported key business initiatives and ROI metrics were tracked and delivered.
• Provided mentorship and guidance to team members, promoting professional growth and technical development. 
• Work with enterprise architects and act as the data engineering expert for target data tech stack gap analysis and tool selection. 
• Lead on a monitoring solution for data pipelines and automated alerts using Grafana and Splunk.

Lead Data Engineer

BCA Marketplace, Surrey, UK                                                                                        Aug 2020 – Jan 2022

• Lead on migration of on-prem SQL DWH to a fully automated Azure cloud CI/CD DEVOPS data pipelines solution resulting in 60% performance improvement on daily updates and enablement of real time analytical use cases.
• Onboard a team of data professionals for operational support to mitigate risk of dependency on 3rd party consultancy and reduce quarterly costs by 40%.
• Introduce ways of working across inhouse and consultancy teams to uplift productivity by 20% and have greater visibility of backlog, team performance and future demand.
• Derive a common data model across 9 EU entities to enable a cross-border valuation training model with a feature store that spanned several languages and other challenging matching criteria for vehicle attribute data. This contributed to an increase of 3-7% in the auction price on average and a better distribution of vehicles across refurbishment centres and capacity utilisation.
• Improved data processing workflows, leading to greater efficiency and shorter turnaround times. 
• Established and maintained data governance frameworks to enhance data quality and compliance across the organisation.
• Partnered with various stakeholders to ensure that data strategies aligned with business objectives, supporting critical projects.
• Work with 3rd Party data suppliers to ingest/transform OEM vehicle specification data ready for data science model training for valuations, buyer recommendation and accuracy models. 
• Championed the introduction of predictive and prescriptive analytics to optimise manufacturing plant machinery uptime, productivity, proactive maintenance as well supply chain car parts demand forecasts.

Lead Data Consultant

Syedina LTD, London, UK                                                                                             Jun 2012 – Aug 2020

Working as a boutique style consultancy, I provided a vast range of BI/DWH solutions to a broad range of clients through MSPs.

• Involved in initial C level data initiative roadmap requirement gathering, engage with delivery leads and Business SMEs to derive SOWs for delivery, lead on ETL patterns discovery and strategy, lead on testing approaches, risk and issues resolution and post production support.
• Project for E-commerce platform
  • Conduct a review/health check on analytical platform, identify data marts required for EMEA region and optimise pipelines which resulted in a 40% performance improvement overnight.
  • Work with EMEA directors to deliver 2 dashboards that highlighted production inefficiency KPI’s that spanned 11,000 product lines.
  • Opportunity identification by using predictive analysis on existing sales data and using market/public data across all EMEA countries resulting in an increase of 20% in sales opportunity pipelines.
  • Budget/Forecast reporting against customer/distributor KPI’s with ability to slice and dice horizontally and vertically by segment/country/product/customer. 
• Project a SAAS platform
  • Upgrade and migrate to new service provider for embedded objects/dashboards on web SAAS platform to monetise white label BI product for client.
  • Conduct health check and feasibility for a streamed solution migration.
  • Provide a merged solution to make all SAAS client environments like for like – (Modular approach) resulting in 90% reduction in deployment time for customers.

Certifications

• Microsoft: Azure Data Engineer Associate; Azure Fundamentals
• Databricks: Data Engineer Associate; Platform Architect
• Databricks Partner Training: GenAI & LLM on Databricks; EDW-ETL Migration; Interoperability & Data Intelligence; SAP Databricks GTM Foundations
• Courses: Digital Transformation & Industry 4.0 (Udemy)Education

Education

Extra Mural Certificate in Economics with Distinction       

Birkbeck University of London, London, UK                                                                                                   Sep 2007 – Jun 2008

Technical Skills

Cloud Platforms: Azure (ADLS, ADF, Stream Analytics, IoT Hub, Synapse, Fabric, SQL, Event Hubs, Functions, VNET, Privatelink, AI Search), AWS, GCP

Data Platforms: Databricks (Apps, Spark, Delta Lake, MLflow, MosaicAI, AI/BI, Unity Catalog, Databricks SQL, Lakeflow, RAG, Vector Search, Delta Sharing, Marketplace, Fivetran), Snowflake, ClickHouse

Streaming & Integration: Kafka, Confluent Kafka, Spark Streaming, API Management

Data Management & Governance: Atlan, Collibra, Purview, Data Quality, Lineage, Metadata, MDM, PostgreSQL, MS SQL Server, MongoDB, Cosmos DB

Programming Languages: Python, SQL, PySpark

DevOps & Infrastructure as Code: Azure DevOps, Terraform, CI/CD, Git, GitHub

Business Intelligence & Monitoring: Power BI, QlikSense, Splunk, Grafana

Industry Domains: Banking, Trading/Investments (MT4/MT5), Insurance, Aviation, Automotive, Pharma, Public Sector, Manufacturing/Supply Chain.