ALPHONSA SIBY

Data Engineer

Bangalore, IN.

About

Highly proficient Data Engineer with over 2 years of experience specializing in building production-grade ETL/ELT pipelines and real-time data ingestion frameworks on Google Cloud Platform (GCP) within financial services. Expert in Python, advanced SQL, and event-driven architectures, adept at consolidating fragmented multi-source data into BigQuery data warehouses to drive critical credit risk, fraud detection, and X-sell modelling initiatives. Proven ability to deliver robust, scalable data solutions that enhance operational efficiency and support data-driven decision-making.

Work

Muthoot Finance Ltd.

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Data and Implementation Engineer

Bangalore, Karnataka, India

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Summary

Designed, delivered, and maintained robust data pipelines, APIs, and ML infrastructure to ensure production reliability and data integrity for financial workflows.

Highlights

Consolidated fragmented multi-source data (SQL Server, GCS, cloud verticals) into a unified BigQuery repository, establishing a single source of truth for credit risk, fraud detection, and propensity modelling.

Built and maintained a quarterly cross-sell base for 3.5 million customers, incorporating daily refresh logic and REST API-based offer lookups to enhance sales strategies.

Designed event-driven data ingestion and allocation pipelines utilizing webhooks and Pub/Sub, achieving low-latency processing for downstream dashboards and analytics.

Enforced comprehensive data governance and security controls, including RBAC, audit monitoring, JWT/API key authentication, and IP whitelisting across sensitive financial data.

Managed GCP and BigQuery operational health through slot monitoring, query performance analysis, and root cause analysis, ensuring 99.9% production reliability for critical pipelines.

Education

Muthoot Institute of Technology and Science

Ernakulam, Kerala, India
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B.Tech Computer Science (Artificial Intelligence) Grade: 8.75 CGPA

St. Sebastian's HSS

Kottayam, Kerala, India
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Senior Secondary Education Grade: 96.5%

Languages

English

Certificates

ISO/IEC 27001 Lead Implementer Certification

Skills

Languages

Python (Pandas, PySpark, NumPy), SQL (Advanced, Complex Queries, Window Functions).

Cloud Platform (GCP)

BigQuery, Cloud Functions, Cloud Run, Pub/Sub, Google Cloud Storage (GCS), Looker Studio, GCP Console, IAM.

Data Engineering

ETL/ELT Pipeline Design, Batch & Real-Time Processing, Data Modelling, Data Normalisation, Data Transformation, Feature Engineering, Event-Driven Architecture, Data Warehousing, Data Lake, Pipeline Orchestration.

APIs & Integration

REST API Development, API Gateway, JWT Authentication, API Key Authentication, IP Whitelisting, Load Balancers, Webhook Integrations, JSON/XML Data Handling.

ML & Analytics

ML Inference Pipeline Operationalisation, Feature Store.

Data Governance & Security

RBAC (Role-Based Access Control), Data Quality Validation, Audit Monitoring, PII Compliance, Data Security, Access Control, Regulatory Compliance.

Databases & Storage

BigQuery (Cloud Data Warehouse), Google Cloud Storage (GCS), Structured & Semi-Structured Data.

Tools & DevOps

VS Code, Postman, Git, GitHub, GCP Console.

Projects

Credit Bureau Data Analytics Pipeline

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Summary

Consolidated fragmented bureau credit data from multiple source systems into a unified BigQuery repository, utilizing SQL Server, GCS, Python (Pandas, PySpark), SQL, and API Integrations. Designed a layered medallion architecture (Raw, Staging, Gold/Feature Layers) and built API-based ingestion pipelines for raw credit report data.

X-Sell Base Creation, Management & API Development

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Summary

Developed and managed an X-sell eligibility base for 2.3 million GL customers using BigQuery, SQL, Python, Cloud Functions, REST APIs, JWT/API Key Authentication, Load Balancer, and IP Whitelisting. Designed daily refresh pipelines and maintained data reliability through deduplication and automated monitoring.

Agentic Bot Lead Generation & Real-Time Call Allocation Pipeline

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Summary

Built a pipeline to fetch agentic bot lead call data into BigQuery using Pub/Sub-triggered allocation to route 'Interested' leads for campaign processing. Utilized Python, SQL, Cloud Functions, SFTP, REST APIs, and Automated Reporting.