Ali Bangash
Data Solutions Architect | Cloud & Lakehouse | ETL, Data Warehouse & Streaming
11+ years designing and building scalable, AI-driven data platforms and ETL/ELT pipelines across healthcare, financial services, and retail. Expert in cloud-native lakehouse architectures, batch & real-time processing, machine learning integration, and enterprise platform strategy.
About
Data Solutions Architect with 11+ years of experience designing and building scalable, AI-driven data platforms and ETL/ELT pipelines across healthcare, financial services, and retail domains. Hands-on expertise in developing cloud-native lakehouse architectures on AWS, Azure, and Databricks, with proficiency in Python, SQL, Apache Spark, Apache Airflow, and Kafka.
Skilled in integrating machine learning workflows, LLMs, and retrieval-augmented generation (RAG) systems to enable intelligent analytics and business insights. Expert in building enterprise data warehouses using Snowflake and BigQuery, with strong experience in data governance, distributed system optimization, and platform architecture.
Adept at leading cross-functional teams, mentoring engineers, and aligning AI and data strategies with organizational goals. Experienced in delivering end-to-end data solutions that combine technical excellence with business impact.
Core Expertise
Cloud Platforms
Data Engineering & ETL
Big Data Technologies
Stream Processing
Databases & Vectors
AI/ML & LLMs
Data Governance & Architecture
Platform & Visualization
Programming & Tools
Domain & Leadership
Professional Experience
Data Solutions Architect
ScienceSoft
FEB 2024 – PRESENT
- Designed and delivered end-to-end data solutions on AWS and Azure, aligning architecture with business requirements across healthcare and financial domains.
- Translated business needs into scalable data architectures, enabling efficient data ingestion, transformation, and analytics workflows.
- Defined and implemented lakehouse solutions using S3, Databricks, and Snowflake to support both batch and real-time analytics use cases.
- Architected intelligent data access solutions by integrating structured datasets with large language model-based querying.
- Designed real-time data processing solutions using Apache Kafka and Spark Streaming for high-volume transactional systems.
- Led development of machine learning solutions using MLflow and cloud-native services such as SageMaker and Azure Machine Learning.
- Established data governance, security, and compliance solutions including data lineage, access control, and regulatory adherence.
- Collaborated with stakeholders and cross-functional teams to define solution architecture and improve system performance and scalability.
Lead Data Engineer
NexHealth
APR 2021 – JAN 2024
- Engineered scalable healthcare pipelines processing EHR and claims datasets with Apache Spark, Python, and Airflow, enabling near real-time analytics for clinical reporting and population health insights.
- Orchestrated HL7 and FHIR ingestion frameworks with Apache NiFi and Kafka, consolidating patient, provider, and clinical records from multiple hospital systems.
- Architected a cloud-based lakehouse architecture on AWS (S3, Glue, Redshift) and Databricks, leveraging Delta Lake to support large-scale healthcare analytics and regulatory reporting.
- Implemented HIPAA-compliant data governance frameworks, including encryption, access controls, and metadata management using AWS Glue Data Catalog and lineage tracking.
- Modeled enterprise data warehouse schemas in Snowflake utilizing Star Schema and Data Vault methodologies to power executive healthcare KPI dashboards.
- Automated monitoring and testing of ETL pipelines using Airflow workflows, CI/CD pipelines, and Docker, improving data reliability and reducing pipeline failures by 35%.
- Produced analytics-ready datasets supporting Tableau and Power BI dashboards, enabling leadership to monitor clinical performance metrics and patient outcomes.
Senior Data Engineer
SentiLink
JAN 2019 – MAR 2021
- Developed large-scale batch and streaming data pipelines using Apache Spark, Kafka, and Hadoop, processing millions of financial transactions for fraud detection and risk analysis.
- Built and optimized distributed data storage solutions using HDFS, Amazon S3, and Hive, enabling scalable analytics across multi-terabyte financial datasets.
- Designed data warehouse solutions using dimensional modeling and Kimball methodology, improving financial reporting performance and supporting real-time business intelligence.
- Automated ETL workflows using Apache Airflow and Talend, enabling seamless ingestion from transactional systems into Amazon Redshift and Snowflake.
- Collaborated with data science teams to deploy machine learning models using Python, Scikit-learn, and MLflow, supporting predictive risk scoring and financial forecasting.
- Implemented data governance and quality validation frameworks, ensuring regulatory compliance and improving data accuracy across reporting systems.
- Delivered executive dashboards (Tableau, Power BI) providing actionable insights into fraud trends, revenue performance, and operational KPIs.
ETL & Data Warehouse Engineer
FourKites
JAN 2015 – DEC 2018
- Developed and maintained enterprise ETL pipelines using Informatica, Talend, and SSIS, integrating high-volume retail and POS datasets into centralized warehouse systems.
- Designed scalable data warehouse architectures (Star & Snowflake schemas) enabling advanced analytics for supply chain and sales performance.
- Led migration of on-premise data systems to AWS and BigQuery, improving scalability and reducing infrastructure costs.
- Built data ingestion pipelines using Apache NiFi, enabling near real-time data flow for inventory and sales tracking.
- Optimized complex SQL queries and Spark jobs, improving performance across large-scale retail datasets.
- Implemented data quality and governance frameworks, ensuring reliable reporting and consistency across business-critical datasets.
- Delivered BI dashboards (Power BI, Tableau) supporting operational decision-making across inventory, sales, and supply chain functions.
Projects
HealthTech Analytics Platform
Designed a scalable healthcare lakehouse on AWS S3 and Databricks using Delta Lake, ingesting HL7/FHIR clinical data from multiple hospital systems.
- Multi-hospital EHR integration
- Population health analytics
- Clinical reporting automation
FinTech Data Platform
Developed real-time streaming pipelines using Kafka, Spark Streaming, and Snowflake to process high-volume financial transactions for fraud detection.
- Real-time fraud detection
- ML feature engineering pipeline
- Risk analytics dashboard
Certifications
Microsoft Certified: Azure Data Engineer
DP-203
Databricks Certified Data Engineer Professional
Professional
AWS Certified Data Analytics
Specialty
Google Professional Data Engineer
Professional
Contact
Interested in discussing data architecture, engineering challenges, or potential opportunities? I'd love to connect.