Jameel Syed
Lead Data Engineer @Costco Wholesale
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WORK HISTORY
Lead Data Engineer @Costco Wholesale
Chicago, IL, US
Led end-to-end data migration projects across on-prem and cloud systems using Azure Data Factory and Azure SQL, ensuring seamless transitions with minimal downtime. Built scalable, high-performance ETL pipelines in Azure Data Factory to integrate data across diverse sources including flat files, on-prem SQL, and cloud storage. Designed and built scalable data pipelines on Azure Data Factory and Databricks to support retail analytics, inventory tracking, and operational reporting Developed data models and curated datasets to enable demand forecasting and supply chain optimization use cases Built high-performance ETL pipelines processing large-scale transactional and inventory datasets, improving pipeline efficiency by 30–40% Partnered with business stakeholders to translate operational requirements into data-driven and predictive solutions Integrated data pipelines with downstream systems and APIs to enable consumption by analytics and external tools Implemented monitoring, logging, and SLA tracking using Azure Log Analytics to ensure reliability of production pipelines Led performance tuning of Spark and SQL workloads to support near real-time analytics use cases Integrated Azure Log Analytics with ADF and Databricks pipelines to monitor performance, automate alerts, and ensure data reliability. Worked with architects to evaluate multiple cloud-native options and recommend Azure Synapse and Cosmos DB for analytical and transactional workloads. Performed data quality validation and reconciliation checks between source and target post-migration using SQL and Python.
EDUCATION
Bellevue University
Master of Science - MS, Computer and information systems
JNTUH College of Engineering Hyderabad
Bachelor's degree, Information Technology
ABOUT JAMEEL SYED
Senior Data and ML Engineer with 13+ years of experience designing and delivering production-grade data and machine learning pipelines on Databricks and Azure. Specialized in building end-to-end predictive data platforms, including feature engineering, model deployment, and ML lifecycle management using PySpark, MLflow, and Delta Lake. Proven ability to bridge the gap between data engineering and business outcomes by translating complex datasets into actionable insights for supply chain, forecasting, and operational decision-making. Experienced in integrating ML outputs into downstream systems and third-party platforms to drive real-world business impact. Expert in Databricks Lakehouse architecture, Azure Data Factory, and distributed data processing, with a strong focus on scalable, high-performance pipelines supporting enterprise AI initiatives. Built end-to-end ML pipelines on Databricks, including feature engineering, model training, validation, and deployment using MLflow. Developed reusable feature pipelines and integrated with feature stores for consistent model training and inference. Designed scalable batch and near real-time inference pipelines using PySpark and Databricks workflows. Integrated ML outputs into downstream systems and external tools to support operational decision-making. Enabled predictive analytics use cases including forecasting, anomaly detection, and optimization. Collaborated with data scientists to productionize models and scale them using distributed Spark processing. Core Skills:Data & ML Engineering:Databricks, PySpark, Spark SQL, MLflow, Feature Engineering, Predictive Modeling, Batch & Streaming PipelinesCloud & Data Platforms:Azure (ADF, ADLS, Synapse), AWS (S3, Glue, EMR), Snowflake, BigQueryData Architecture:Delta Lake, Medallion Architecture (Bronze/Silver/Gold), Data Modeling (Star, 3NF), Lakehouse DesignAI / ML Capabilities:Forecasting Pipelines, Time-Series Data Processing, Feature Store Integration, Model Deployment, Model MonitoringIntegration & Orchestration:Databricks Workflows, ADF Pipelines, REST APIs, External Tool IntegrationProgramming:Python, PySpark, SQL, Scala, Spark
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