Pune District, Maharashtra, India
Data Engineer | Python | SQL | PySpark | Airflow | Snowflake | AWS I am a Data Engineer with 5+ years of experience building scalable, reliable data pipelines that turn raw data into meaningful insights. I work extensively with Python and SQL for data transformation and analysis, leveraging Pandas and NumPy for efficient data processing. My hands-on experience includes PySpark for distributed data processing, Apache Airflow for orchestrating complex workflows, and Snowflake for cloud-based data warehousing. I focus on designing robust ETL/ELT pipelines, improving data quality, and optimizing performance to support analytics and decision-making at scale. With a strong foundation in enterprise systems, I bring a structured, problem-solving mindset to modern data engineering challenges and collaborate closely with stakeholders to deliver impactful, production-ready solutions. I’m continuously learning and adapting to new tools and best practices in the evolving data ecosystem. Key Skills: Python, SQL, PySpark, Apache Airflow, Snowflake, ETL/ELT, Data Pipelines, Data Warehousing, Data Modeling. 🍵 Let’s Connect When I’m not engineering data flows or exploring cloud platforms, you’ll find me experimenting in the kitchen, reflecting on spiritual philosophies, or getting lost in a good thriller. I’m always up for a chat—especially over a cup of tea!
Worked in the cards and payments domain with European fintech clients, supporting data-driven payment processing and analytics platforms. Led end-to-end requirement gathering and analysis, collaborating directly with business stakeholders to translate payment, reconciliation, and compliance requirements into data engineering solutions. Designed and supported ETL pipelines to ingest, transform, and validate payment transaction data across authorization, clearing, and settlement stages. Worked with cloud-based data platforms to enable scalable processing, storage, and analytics of high-volume payment datasets. Gained hands-on exposure to Mastercard and Visa networks, including transaction formats, settlement files, and reporting data. Performed data validation, reconciliation, and quality checks to ensure accuracy, regulatory compliance, and reliable financial reporting. Enabled analytics and reporting use cases for payment performance, transaction monitoring, and business insights. Collaborated with cross-functional engineering, QA, and compliance teams to deliver secure, high-performance data pipelines aligned with fintech standards.
Designed and supported large-scale batch data processing pipelines for US healthcare and insurance clients, handling high-volume structured datasets with strict accuracy and compliance requirements. Built and maintained ETL-style workflows (batch and near-real-time) to ingest, transform, and validate enterprise data, aligning legacy data flows with modern cloud data engineering patterns. Integrated multiple upstream and downstream systems using API-based data ingestion, enabling reliable data exchange across heterogeneous platforms and analytics consumers. Collaborated with onsite stakeholders to gather business and data requirements, translate them into scalable data solutions, and provide accurate effort and delivery estimates. Implemented rigorous data quality checks, reconciliation logic, and exception handling to ensure pipeline reliability, completeness, and consistency. Performed end-to-end testing of batch, online, and API-driven data pipelines, ensuring fault tolerance and operational stability across environments. Applied strong understanding of schema-driven data modeling, metadata management, and structured data storage to support reporting and downstream analytics. Leveraged enterprise data governance practices to ensure secure, auditable, and compliant data processing—critical for healthcare and insurance workloads.
1. Passed training and Assessment for all the required skill.