Shaik Shahina
Senior Data Engineer • S**********@gmail.com • +13******856 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Senior Data Engineer with 8+ years of experience designing, building and operating large-scale ETL/ELT pipelines, data lakes and cloud data warehouses for financial services, logistics and e-commerce. Strong hands-on expertise in Python, SQL and PL/SQL, Apache Spark, Kafka, Airflow and Informatica, and production cloud platforms (AWS, S3, Redshift) paired with Terraform and container orchestration. Experienced with Teradata and Oracle-based enterprise warehouses and with deploying CI/CD pipelines using Jenkins and Git. Delivered streaming and batch solutions that feed analytics, regulatory reporting and fraud systems, and processed datasets ranging from millions to billions of records. Proven in data modeling, governance and mentoring engineers to deliver robust, maintainable pipelines that support analytics and machine learning at scale.
Technical Skills
Programming Language: Python,R,SQL
Backend Technologies: Django,Flask,Django REST Framework,RESTful APIs
Databases: Teradata,Oracle,Amazon Redshift
Cloud Platforms: Amazon Web Services
Version Control & Development Tools: Git
DevOps & Infrastructure: Terraform,Jenkins,Docker,Kubernetes
Messaging & Monitoring: Apache Kafka
Data Engineering & Processing: Apache Spark,Apache Airflow,Apache Beam
Data Warehousing: Data Modeling
Data Analysis & Visualization: Pandas,NumPy,Tableau,PL,Informatica PowerCenter
AI/ML Frameworks & Libraries: scikit-learn,Data Governance
Project Management & Collaboration: A
Work Experience
Barclays (Client)
Whippany, NJ
Senior Data Engineer
April 2023 – Present
Worked at a large banking/financial services organization; built and maintained data pipelines and warehouses that support regulatory reporting, analytics and operational BI.
Tech Stack: Python, Apache Airflow, PL, SQL, Teradata, Apache Kafka, AWS S3, Amazon Redshift, Informatica PowerCenter, Jenkins, Git, Docker, Kubernetes, Terraform
- Designed and implemented scalable batch ETL pipelines using Python and Apache Airflow to ingest multi-source financial data for reporting and analysis.
- Implemented complex SQL and PL/SQL routines to support regulatory reporting and BI consumption across Teradata and Oracle.
- Developed Spark jobs to process large transaction datasets and optimize heavy joins and aggregations for downstream analytics.
- Built near real-time streaming pipelines using Apache Kafka and evaluated Apache Beam prototypes for stream ingestion into Teradata.
- Led migration of select on-prem ETL workloads to AWS S3 and Amazon Redshift to increase storage elasticity and simplify processing.
- Provisioned cloud infrastructure with Terraform and configured Kubernetes clusters for container orchestration.
- Automated CI/CD for pipeline deployments using Jenkins and Git, packaging pipeline code into Docker images for reproducible releases.
- Enforced data governance by implementing automated data quality checks with Informatica PowerCenter and Python while mentoring junior engineers on coding standards.
Insurance Auto Auction (Client)
Westchester, IL
Data Scientist
June 2021 – December 2022
Delivered analytics and predictive models for an insurance/auction services client; supported forecasting, classification and risk modeling use cases.
Tech Stack: Python, scikit-learn, Pandas, NumPy, R, SQL
- Developed end-to-end data science solutions using Python and scikit-learn to support forecasting and classification business use cases.
- Performed exploratory data analysis and feature engineering with Pandas and NumPy to prepare structured datasets for modeling.
- Designed preprocessing pipelines to handle missing values, outliers and categorical encoding using Python and SQL extractions.
- Applied statistical tests in R and Python and used cross-validation to validate model performance and ensure robustness.
- Built time-series forecasting models and exported model artifacts for downstream deployment and operational consumption.
- Monitored model performance post-deployment and collaborated with data engineering teams to automate retraining workflows.
UPS (Client)
Timonium, MD
Data Analyst
April 2019 – April 2021
Supported payments and fraud analytics for a global logistics/payments organization; built reporting pipelines and analytics to detect fraud and support operations.
Tech Stack: SQL, Python, Pandas, NumPy, Apache Spark, Teradata, Oracle, Tableau
- Analyzed high-volume global payment transaction datasets consisting of millions to billions of records using SQL and Python to identify fraud indicators.
- Designed and optimized complex SQL queries and Teradata/Oracle data models to improve retrieval efficiency for fraud detection workflows.
- Developed end-to-end ETL workflows using Python and Apache Spark to extract, cleanse and load payment data into analytical models.
- Built executive dashboards in Tableau to surface transaction KPIs, authorization trends and merchant performance for leadership.
- Performed EDA with Pandas and NumPy to detect anomalies and inform statistical fraud models and feature design.
- Implemented data quality validation frameworks with SQL and Python to ensure audit-ready datasets for regulatory reporting.
- Partnered with fraud risk and cybersecurity teams to tune detection rules and evaluate A/B test results for authorization strategies.
NanoMindz Solutions
Visakhapatnam, INDIA
Software Engineer
June 2015 – November 2016
Worked at an e-commerce engineering firm building backend services, pricing engines and operational tools for large catalog and order systems.
Tech Stack: Python, Django, Django REST Framework, Flask, Pandas, SQL Server, ReactJS, RESTful APIs
- Built dynamic pricing backend logic in Python that adjusted prices based on competitor data, inventory and demand signals for a catalog of 50M+ product listings.
- Developed order lifecycle services and RESTful APIs with Django and Django REST Framework to handle cart calculations, discounts and payment reconciliation.
- Designed and implemented batch processing pipelines with Pandas to process time-series pricing and inventory datasets, achieving 99.9% data accuracy during data migrations.
- Processed and validated high-volume batch datasets using SQL Server to support time-series analysis for pricing and inventory movement.
- Created internal dashboards and operational tools with Flask and ReactJS, significantly reducing manual reporting effort for business users.
- Implemented Python utilities and validation scripts to automate data quality checks and improve downstream reporting consistency.
Education
Governors State University
Master’s Degree, Computer Science • Illinois • 2018
GMR Institute of Technology
Bachelor’s Degree, Computer Science & Engineering • India • 2015
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