Fatima Sayeda
Azure Data Engineer @Procter & Gamble
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WORK HISTORY
Azure Data Engineer @Procter & Gamble
Delivered ingestion and transformation pipelines using an established object-oriented framework, standardising implementation across multiple workstreams and reducing duplicated ADF logic through reusable patterns.Translated ambiguous stakeholder requirements into production-ready data solutions by defining data contracts, validation rules, test scenarios, and acceptance criteria; produced operational runbooks to support BAU ownership.Built dimensional models using Kimball methodology (conformed dimensions, facts, SCD handling) to enable consistent cross-mart reporting and audit-ready metrics.Engineered Databricks ETL using Python/PySpark/Scala for data movement, Delta Lake publishing, and analytics workflows supporting global reporting packages.Developed SharePoint API ingestion using Databricks + Python, implementing pagination, throttling/backoff, schema handling, and failure recovery to improve reliability and adoption.Provided design reviews and implementation guidance to enforce engineering standards across deliveries (naming/versioning, CI/CD, monitoring, release discipline).
EDUCATION
Kingston University
Master of Science; Accounting and Finance, Accounting
Chartered Institute of Management Accountants
CIMA QUALIFIED, Accounting
ABOUT FATIMA SAYEDA
Azure Data Engineer with 7+ years of experience building and operating enterprise data platforms and automation across finance and operational domains. Delivers metadata-driven ingestion frameworks, resilient orchestration, dimensional data models, and quality-first delivery practices enabling auditable reporting at scale.Deep expertise across Azure Data Factory, Azure Databricks, Delta Lake, and Azure SQL, backed by production engineering (Python, PySpark/Scala, SQL, Git, CI/CD). Known for standardising project-specific pipelines into reusable platform patterns, improving reliability and observability, and reducing operational overhead through automation and repeatable delivery frameworks. Trusted to translate ambiguous requirements into production-grade implementations and to drive engineering standards across stakeholders.Independently delivers solution architecture and Infrastructure-as-Code (Terraform, Bicep) and builds secure agentic AI (RAG) applications with vector and graph data integration. Security focus includes identity-based access control, secret management, environment isolation, data boundary enforcement, and controlled retrieval patterns aligned to enterprise deployment requirements.
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