I'm Abdullah Shahid — a data engineering specialist with 6+ years building enterprise-grade pipelines, lakehouses, and streaming systems. I work with any kind of data — structured, semi-structured, unstructured — across any cloud.
I'm a Senior Data Engineer and Data Architect with 6+ years designing enterprise data infrastructure from the ground up. I've progressed from engineer to architect through consistent delivery, technical leadership, and a relentless focus on building things right.
What sets me apart is my breadth — I work with any kind of data: relational, document, streaming, time-series, semi-structured, and unstructured. Whether it's batch ETL, real-time Kafka streams, or ML feature pipelines, I design and build systems that are reliable, observable, and scalable.
Currently at AlphaBridge Consulting, I lead data architecture engagements for international clients — designing multi-cloud lakehouses, defining data governance standards, and implementing DataOps practices that make data teams 10x more productive.
Leading end-to-end data architecture design for enterprise clients across multiple industries. Architecting cloud-native data lakehouses, building real-time streaming pipelines, and implementing DataOps best practices. Primary technical lead for cross-functional data strategy and international client engagements.
Promoted within first year for exceptional delivery. Led architectural decisions for the data platform, migrated legacy batch systems to cloud-native streaming, and introduced dbt as the standard transformation layer — dramatically improving data quality and team velocity.
Built and maintained robust data ingestion and transformation pipelines for analytics and BI reporting. Developed Python-based frameworks, optimized complex SQL queries on large-scale warehouses, and collaborated with analysts to deliver reliable data products.
Started my career building ETL workflows and batch pipelines powering key BI dashboards. Gained deep expertise in Python, SQL and cloud data fundamentals — the technical bedrock everything since has been built upon.
Designed and built a scalable data lakehouse for a global retail client processing 5TB+ of daily transactions. Unified structured POS data, semi-structured clickstream events, and unstructured customer feedback into a single Medallion architecture.
Built a high-throughput event streaming pipeline for a fintech client to detect fraud in real time. Ingested millions of transactions per day, applied streaming transformations, and fed results into downstream ML models with sub-second latency.
Led a full DataOps transformation for a legacy data warehouse — migrating on-premise ETL scripts to a cloud-native ELT stack with full CI/CD, automated data quality testing, lineage tracking, and self-service analytics capabilities.
Engineered a scalable feature pipeline for a recommendation engine serving 2M+ daily users. Built batch and real-time feature computation, feature store integration, and automated retraining triggers using PySpark on Databricks.
Built an end-to-end IoT data ingestion and analytics platform for a manufacturing client. Collected high-frequency sensor readings from 500+ devices, applied time-series anomaly detection, and delivered real-time dashboards for predictive maintenance.
Designed a document intelligence pipeline processing unstructured text from contracts, emails, and reports. Used NLP preprocessing, entity extraction, and vector embeddings to make millions of documents searchable and analytically useful.
I'm actively looking for Senior Data Engineer, Data Architect, and DataOps roles at international companies — especially remote-first teams solving hard data problems at scale.