Prabhuling G.

Data Engineer

Pune, Maharashtra, India3 yrs 9 mos experience

Most Likely To Switch

Key Highlights

Expert in Azure Data Engineering with 3+ years experience.
Proven track record in building scalable data pipelines.
Strong background in banking data integration and compliance.

Stackforce AI infers this person is a Data Engineering specialist in the Healthcare and Fintech sectors.

Contact

gadekarprabhu1822@gmail.com LinkedIn

Skills

Core Skills

Data EngineeringData PipelinesEtl

Other Skills

ADFS 2.0Apache AirflowApache SparkAzure Data FactoryAzure DatabricksCommunicationData LakesData LoadingData QualityData ScrapingData StructuresExploratory Data AnalysisExtract, Transform, Load (ETL)JavaJavaScript

About

I’m a results-driven Azure Data Engineer with 3+ years of experience designing and implementing scalable, secure, and high-performance data pipelines in cloud environments. My expertise lies in leveraging Azure Data Factory, Databricks (PySpark), and Data Lake Storage (ADLS Gen2) to build modern data lakehouse architectures. 🏦 I’ve worked extensively on banking data integration, handling ISO 20022, SWIFT, and core banking systems, while ensuring regulatory compliance (such as PCI DSS and GDPR) through robust data masking, encryption, and role-based access controls. I specialize in transforming raw transactional and financial data into actionable insights for reporting and analytics using Power BI and Azure Synapse. ⚙️ Skilled in data ingestion, cleansing, and transformation, I’ve built end-to-end ETL/ELT pipelines that support real-time monitoring and historical analytics. I’m passionate about improving data quality, automating workflows, and driving better decision-making with clean, reliable data. 💡 I believe in continuous learning, cross-team collaboration, and building systems that not only work — but scale and evolve with business needs. ✉️ Feel free to reach out to me at gadekarprabhu1822@gmail.com to connect, collaborate, or explore exciting opportunities in front-end and React.js development.

Experience

Confidential

Software Engineer

Dec 2023 – Present · 2 yrs 3 mos · Pune, Maharashtra, India · On-site

we created two key data pipelines for member and claims data using Azure Data
Factory (ADF) and Azure Databricks.
The pipelines start by checking for the presence of JSON files and logging the success or failure of each
step. In Databricks, we validate the file schemas and archive any mismatched files.
Valid files are then cleaned, transformed (e.g., removing duplicates, filling missing values), and loaded into
Synapse for reporting.
Throughout the process, we maintain comprehensive logging and use SQL views for dynamic data
extraction, ensuring a reliable and flexible data pipeline.
Automated a scalable data pipeline, enhancing efficiency, data quality, and compliance while enabling
reliable reporting and analytics in Azure Synapse.
Delivered structured data for advanced analytics, supporting healthcare insights and large-scale data
processing with improved reporting capabilities.

Azure Data FactoryAzure DatabricksSQLData PipelinesData QualityData Engineering

Ncs group

Data Engineer

May 2022 – Nov 2023 · 1 yr 6 mos · Pune, Maharashtra, India

Designed and developed robust, scalable, and efficient data pipelines for batch and real-time data ingestion and processing.
Implemented ETL/ELT workflows to extract, transform, and load data from diverse sources into data lakes and warehouses.
Built and maintained data models and optimized SQL queries to support analytics and reporting use cases.
Integrated structured and unstructured data from various internal and external sources, ensuring high data quality and reliability.
Collaborated closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver actionable insights.

ETLData LakesSQLData QualityData Engineering

Pepcoding

Data Engineer

Dec 2021 – May 2022 · 5 mos

Designed and developed robust, scalable, and efficient data pipelines for batch and real-time data ingestion and processing.
Automated data workflows using tools like Azure Data Factory and Apache Airflow to ensure timely and reliable data delivery.
Leveraged big data tools such as Apache Spark and Azure Databricks for large-scale data processing and transformation.
Ensured data security, compliance, and governance by implementing access controls, data lineage, and monitoring strategies

Azure Data FactoryApache AirflowApache SparkData EngineeringData Pipelines