Arslan

Enterprise Data Architect with 8+ years of experience delivering enterprise data platforms, ETL/ELT pipelines, data warehouses, and distributed processing applications. Combines hands-on Azure, Databricks, Spark, PySpark, Python, and SQL engineering with architecture design and technical leadership.

Experience includes batch and streaming processing, dimensional modeling, data quality, query tuning, and Power BI analytics. Integrates enterprise sources and supports cloud migration and reconciliation. Guides architecture decisions, code reviews, deployments, and production troubleshooting. Consulting capabilities cover Azure modernization, Microsoft Fabric, and Snowflake.

Enterprise Data Consultant / Enterprise Data Architecture - Enterprium

(2025-01)

• Design data warehouses, lakes, and lakehouses for enterprise integration and analytics, aligning data models and pipelines with client requirements.
• Develop batch and streaming ETL/ELT pipelines using Azure Databricks, Spark, PySpark, Python, and SQL.
• Modernize Azure data workflows and integrate enterprise sources with downstream reporting systems.
• Build Power BI solutions using DAX, Power Query, and semantic models to support business reporting.
• Diagnose pipeline failures and tune Spark workloads and SQL queries for reliable production processing.
• Guide architecture reviews, data quality, and governance across client delivery and production support.

Sr Data Engineer - IBM - United States

(2022-11 - 2024-12)

• Architected Azure Databricks batch and streaming ETL/ELT workflows and relational models.
• Optimized Spark, PySpark, and Scala workloads for distributed processing performance.
• Designed distributed workloads using Azure Service Fabric and secure containers; provisioned environments with Terraform.
• Integrated data quality tests and access controls into platform designs for governance and compliance.
• Guided architecture decisions and code reviews to align engineering delivery with platform standards.
• Led ETL/ELT delivery for billions of records under production service-level agreements (SLAs).
• Designed dimensional models and relational schemas for enterprise reporting and analytical data products.
• Partnered with cross-functional teams to define requirements and deliver data products for self-service analytics.
• Established data quality, lineage, metadata, and privacy controls for governed analytical datasets.
• Developed CI/CD practices for consistent deployment and maintenance of data pipelines.

Sr Big Data Developer - S&P Global - Boston, Massachusetts, United States

(2019-08 - 2022-10)

• Built fault-tolerant distributed batch and event-streaming applications using Apache Spark.
• Optimized Spark memory, shuffles, and cluster settings; worked with Parquet, Avro, and ORC storage formats.
• Managed workflow dependencies and data quality checks to coordinate reliable processing.
• Handled schema drift and investigated distributed processing failures to support dependable downstream data.
• Developed SQL transformations and ETL pipelines integrating relational databases, APIs, CSV, and JSON into staging and warehouse layers.
• Built incremental loads with Azure Data Factory and Python; supported selected SQL and SSIS migrations to Azure.
• Tuned SQL and stored procedures, reconciled data, and investigated failed runs; supported Azure SQL and Power BI models.

BS - Software Engineering - Boston College of Engineering (2019-07)