Senior Data Engineer Banking & Payments (AWS/Snowflake)

Suswara kandula

Contact no. gmail id LinkedIn URL

SUMMARY

Data Engineer with over four years of experience building and supporting cloud-based data platforms within the banking and payments domain. Experienced in working with large-scale financial and transactional data, including card activity, customer behavior, merchant analytics, and regulatory reporting. Proven ability to develop reliable batch data pipelines, analytics datasets, and reporting-focused data models using AWS, Apache Spark, and Snowflake. Strong in Python and SQL, with a focus on data quality, performance optimization, and supporting analytics and AI/ML initiatives in Agile environments.

TECHNICAL SKILLS

●Programming & Querying: Python, SQL

●Data Engineering & Platforms: Apache Spark, dbt, Apache Airflow, ETL/ELT Pipelines, Batch Processing

●Cloud & Warehousing: AWS (S3, IAM, CloudWatch), Snowflake, Databricks

●Data Modeling & Quality: Fact & Dimension Modeling, Query Optimization, Data Validation, Reconciliation, Data Monitoring

●Domain Expertise: Banking & Payments, Financial & Transactional Data, Card Transactions, Customer & Merchant Analytics, Regulatory & Operational Reporting

WORK EXPERIENCE

Fifth Third Bank, USA July 2025 – Present

Data Engineer

●Built and ran cloud-based batch data workflows on AWS, supporting daily and scheduled processing of financial and transactional data for reporting and analytics teams.

●Developed and maintained ETL/ELT pipelines using Spark, dbt, and Apache Airflow, handling incremental loads and backfills while improving on-time pipeline completion by 25%.

●Created and managed analytics datasets in Snowflake and Databricks, structuring data around business KPIs and helping analytics teams access reliable data 20–30% faster.

●Used Python and SQL for data transformations, validation logic, and automation, improving overall data reliability and reducing manual effort.

●Improved query and data model performance through schema design, partitioning, and query refactoring, reducing execution times by 30–40%.

●Added data quality, reconciliation, and monitoring checks, cutting down repeat reporting issues by 30%.

●Built feature-ready datasets and supporting pipelines for AI and machine learning use cases, while partnering with analytics, data science, and governance teams to deliver compliant, well-documented data assets.

American Express, India May 2020 – August 2023

Data Engineer

●Started as a graduate-level data engineer, supporting enterprise analytics and reporting platforms focused on card transactions, customer behavior, and merchant performance.

●Built and maintained ETL pipelines using Python and SQL, handling incremental loads and transforming data from transaction systems and operational sources for reporting.

●Implemented and supported batch workflows using Apache Airflow, managing scheduling, monitoring, backfills, and reprocessing to keep pipelines reliable.

●Built and managed curated analytics datasets using AWS S3 for staging and Snowflake as the analytics warehouse, ensuring consistent and repeatable data loads.

●Added data quality, validation, and reconciliation checks, along with basic monitoring, to catch missing or inconsistent data in reporting datasets.

●Supported reporting-focused data models (fact and dimension tables) and optimized SQL queries, contributing to 30–40% faster dashboard refresh and report generation times.

●Gained hands-on experience with Git-based version control, code reviews, and basic production support, working closely with senior engineers in an Agile/Scrum environment while supporting dashboards in Tableau and Power BI.

EDUCATION

Masters in

Contact this candidate