Shifa Maknojia

About Shifa Maknojia

Data Engineer with 4+ years of experience designing and delivering scalable batch and near real-time data pipelines across healthcare and banking domains- Experienced in building modern ELT architectures using AWS, Azure, Snowflake, dbt, Spark, and Kafka, processing multi-million record datasets for regulatory, risk, and analytics use cases- Strong background in multi-cloud data platforms, leveraging AWS for ingestion and storage and Azure for analytics workloads within regulated financial environments- Proficient in developing dimensional data models including fact/dimension tables, surrogate keys, and SCD Type 2 implementations to support compliance and enterprise reporting- Hands-on experience implementing data quality frameworks, dbt testing (unique, not-null, referential integrity), and reconciliation checks to improve data reliability and reporting accuracy- Strong programming expertise in Python and SQL, building reusable data processing modules, optimizing Spark transformations, and developing maintainable pipeline logic for production environments- Enabled early-stage AI/ML initiatives by building curated feature datasets and supporting fraud detection and transaction monitoring workflows- Comfortable operating in HIPAA- and AML-regulated environments, applying RBAC, encryption, CI/CD practices, and Agile delivery models to ensure secure and reliable data solutions.

Experience

1. Data Engineer

   UMB Bank

   Jul 2023 — Present · Kansas City, MO, US

   Designed and owned end-to-end scalable ELT pipelines across AWS (S3, Glue) and Azure (ADLS, Synapse), processing 8–12M transaction and risk records daily- Built cloud-native ingestion workflows using Python, SQL, and Spark for core banking datasets (transactions, loans, KYC), reducing batch latency by 30%- Enabled near real-time ingestion via Apache Kafka, supporting fraud detection and AML monitoring with sub-hour data availability- Developed modular dbt models in Snowflake (multi-cloud), implementing staging-to-mart layers, fact/dimension tables, surrogate keys, and SCD Type 2 for risk reporting- Implemented robust dbt and SQL validation tests (uniqueness, null, referential integrity), improving data quality and reducing reporting discrepancies- Orchestrated cross-cloud workflows using Airflow and Azure Data Factory, maintaining 99%+ SLA adherence for batch and streaming pipelines- Optimized Snowflake warehouses (auto-suspend, scaling, clustering), reducing runtime 20–25% and improving BI performance- Built curated feature datasets and batch feature pipelines supporting fraud and transaction monitoring ML models- Managed Git-based version control and CI/CD for dbt and ELT deployments, reducing production incidents- Partnered with platform teams using Terraform to provision cloud storage and analytics resources across AWS and Azure- Enforced governance and security best practices (RBAC, encryption at rest/in transit) for sensitive financial data- Owned AML regulatory reporting pipelines in an Agile/Scrum environment, ensuring compliance-driven release timelines.

Education

• University of Houston-Downtown

  Bachelor of Science in Computer Science - BSCS

• Lone Star College

  Associate of Science in Computer Science - ASCS

• The University of Texas at Austin

  Master of Science in Artificial Intelligence - MSAI