Headline

Data Engineer | GCP (BigQuery, Dataflow, Cloud Spanner, Pub/Sub) | PySpark | Real-Time Payments | Google Cloud Certified: Professional Data Engineer & Database Engineer

About

I build cloud-native data platforms on Google Cloud that move millions of payment records reliably and fast. Over ~4 years at Wipro, I’ve worked on real-time and batch data engineering for Lloyds Banking Group (commercial payments, fund checks) and Nomad Foods (European supply chain data). What I work on: • Streaming and batch pipelines: Dataflow (Apache Beam), Kafka, Pub/Sub, PySpark on Dataproc • BigQuery and Cloud Spanner: schema design, query optimization, partitioning, data skew handling • Data modelling: logical and physical models with ER/Studio • Reliability and security: IAM and service accounts, data quality checks, production support under strict SLAs in PCI-DSS environments Selected results: • ~25% lower end-to-end latency on real-time payment event streams • ~30% faster query execution through Cloud Spanner schema design • Optimized the fund check query that runs on every payment request (100+ per hour) • ~50% less manual reconciliation effort and ~35% better pipeline reliability at Nomad Foods Certified: Google Cloud Professional Data Engineer and Database Engineer, AWS Certified Data Engineer Associate, AWS Cloud Practitioner, Azure AZ-900. Tools: Python, SQL, PySpark, Java, BigQuery, Cloud Spanner, Dataflow, Dataproc, Cloud Composer / Airflow, Kafka, Pub/Sub, Terraform, GKE, Git

Location

Bengaluru

AI stack

claude code

github copilot

opencode

ChatGPT

Work experience

Data Engineer at Wipro, Dec 2022 - Present · 4 yrs, Bengaluru, Karnataka, India, Data Engineer on enterprise GCP data platforms for Lloyds Banking Group (commercial payments) and Nomad Foods (European supply chain data). Lloyds Banking Group | Jan 2025 - Present • Designed the Dataflow (Apache Beam) architecture and built 6 pipelines (3 historical load, 3 delta) ingesting Kafka payment events into Cloud Spanner and BigQuery with sub-minute latency • Cut end-to-end processing latency by ~25% by tuning Kafka payload handling, partitioning and Beam transformations • Optimized the fund check SQL query that runs on every payment request (100+ per hour), making it ~17% faster • Built logical and physical data models in ER/Studio; Cloud Spanner schema design improved query time by ~30% • Developed PySpark jobs on Dataproc using repartitioning and broadcast joins • Defined IAM roles and service account policies, handled late-arriving data, and maintained 99.9%+ pipeline uptime under strict SLAs in a PCI-DSS environment Nomad Foods | Dec 2022 - Dec 2024 • Built and orchestrated ETL/ELT pipelines with Apache Airflow; monitored DAGs, analysed logs and fixed failures (MTTR down ~30%) • Developed BigQuery and Pub/Sub pipelines with partitioned tables; resolved data skew and enforced partition filters to control query cost • Built validation, data quality, deduplication and error-handling frameworks for payment data in PostgreSQL: ~50% less manual reconciliation, ~35% better reliability • Cut FTPS and SharePoint ingestion from hours to minutes using partitioning and parallel execution

Intern at Madhulata Infotech, Jan 2022 - Dec 2022 · 1 yr, Delhi, India, Maintained and optimized SQL databases, ensuring high performance and availability. Participated in ETL processes, extracting data from various sources for integration. Developed and optimized SQL queries for efficient data retrieval and manipulation. Collaborated with cross-functional teams to understand data requirements and deliver tailored solutions. Provided support in troubleshooting database issues and performance tuning to enhance database reliability.

Education

Bachelor of Technology - BTech, Computer Science, Chandigarh Group Of Colleges

Inter, Mathematics and Computer Science, M.R.J.D Inter College