Engineering
Python, SQL, PySpark and Spark workflows for ingestion, cleansing, transformation, enrichment and reconciliation.
DATA ENGINEERING • PUNE, INDIA
Building reliable
data platforms
from source to insight.
Data Engineer focused on Python, SQL, PySpark, Apache Spark, Azure Databricks, ETL/ELT, data quality and production-grade analytics workflows.
3+ yrsCore Data Engineering
Financial ServicesPortfolio Analytics
Python + SQLPySpark / Spark
Azure Databricks
AWS • Spark • Kafka
ETL / ELT • DQ • CI/CD
01 / ABOUT
Data engineering with a production mindset.
My work spans enterprise ETL, data validation, reconciliation, distributed processing, analytics products and cloud modernization. I focus on traceable pipelines, clean data contracts, operational reliability and useful business outcomes.
Cloud & Platforms
Azure Data Factory, Azure Databricks, Azure Data Lake, AWS S3 and AWS Glue/Lambda/Redshift exposure.
Quality & Reliability
Schema checks, null validation, business rules, source-to-target reconciliation, retries and operational runbooks.
Analytics
Power BI, DAX, SQL analytics and data products that turn curated datasets into business-facing KPIs.
02 / EXPERIENCE
Enterprise data engineering experience.
Infosys Limited — Data Engineer
Capital Group Portfolio Analytics · Financial Services
- Built PySpark, Python and SQL ETL workflows for high-volume investment datasets across CSV, JSON, Parquet, Azure Data Lake and AWS S3.
- Implemented schema validation, null checks, business-rule validation and source-to-target reconciliation for quality-controlled deliverables.
- Optimized SQL Server analytics for NAV, risk, fund performance and KPI reporting.
- Supported modernization of legacy sources into a centralized Azure analytics platform using Azure Databricks.
- Automated reporting and deployment activities with Python, Git and Jenkins.
Infosys Limited — Data Engineer
Broadband Customer Churn Analytics · Telecommunications
- Built PySpark ETL for telecom usage, demographic, CSV, Parquet, JSON and log data.
- Developed SQL transformations using joins, CTEs and window functions for churn features and customer behavior indicators.
- Implemented ETL testing across data-lake and warehouse layers.
- Used SSIS for scheduled batch movement and supported CI/CD delivery.
- Built churn analysis and Power BI dashboards for risk segments and KPIs.
Mphasis Limited — MIS Executive / Data Analyst
Klarna US Customer Behavior & Fintech Analytics
- Analyzed transaction data with SQL and Python for spend behavior, cohorts and customer segments.
- Built PySpark workflows from AWS S3/Azure Blob Storage into analytical datasets.
- Applied schema checks, null handling and reconciliation controls.
- Built Power BI dashboards and predictive customer-behavior analysis.
03 / TECHNICAL STACK
The tools I use to move data reliably.
Programming & Processing
Azure
AWS
Data Engineering
Data Platforms
Databases & BI
DevOps & Delivery
Observability
04 / PROJECTS
Projects that connect engineering to business outcomes.
01
FINANCIAL SERVICES
Capital Group Portfolio Analytics
Enterprise investment-data ETL covering ingestion, cleansing, reconciliation, analytics and modernization across Azure and AWS data sources.
Sources→PySpark→Curated Data→KPIs
02
TELECOMMUNICATIONS
Broadband Customer Churn Analytics
High-volume ETL and analytical preparation for customer behavior, churn features, data quality and Power BI reporting.
03
PERSONAL ARCHITECTURE PROJECT
Global Investment Banking Market Data Platform
A production-oriented blueprint for global stock, ETF, index and financial data using streaming + daily batch, Kafka/MSK, Spark, S3, Airflow/MWAA, data quality, governance and automated reporting.
04
FINTECH ANALYTICS
Klarna US Customer Behavior
Transaction analytics and PySpark ETL for customer segmentation, cohort trends, conversion, AOV and anomaly monitoring.
05 / ARCHITECTURE GALLERY
From concept to production-grade market-data platform.
This gallery captures the architecture evolution: ingestion, Kafka/MSK, Spark, bronze/silver/gold layers, orchestration, quality, governance, CI/CD, DR and cost controls.
FINAL BLUEPRINT
Global Investment Banking Market Data Platform
Real-time + daily batch architecture with AWS managed services, schema registry, DLQ/retry, idempotency, timezone handling, backfill, security, governance, high availability, multi-region DR and automated email reports.
- Global stock exchanges, APIs, WebSockets and corporate actions
- MSK/Kafka topics with Avro/Protobuf schema controls
- EMR Serverless / Spark for streaming and batch processing
- S3 Bronze → Silver → Gold with Parquet/Delta/Iceberg patterns
- RDS/Aurora, Redis, Athena, Redshift and QuickSight serving paths
- MWAA orchestration, CloudWatch/X-Ray, IAM/KMS/Lake Formation
- Multi-region DR, data residency and RTO/RPO controls
01 — Core Market Data Architecture
Initial streaming + batch design using Python ingestion, Kafka, Spark, HDFS/MinIO, PostgreSQL, Airflow and analytics tooling.
02 — AWS Native Mapping
AWS-managed mapping for ingestion, streaming, processing, S3 lake storage, serving and analytics.
03 — Reliability & Backfill
Adds DLQ, retries, idempotency, checkpointing, timezone-aware scheduling and reprocessing controls.
04 — CI/CD & Deployment
Introduces source control, build/test, encrypted artifacts, infrastructure deployment, approval and rollback.
05 — Operational Risk Controls
Adds schema validation, approval gates, API rate limits, governance and production safeguards.
06 — Rate Limits & Governance
Strengthens API quota management, compliance, access governance, data residency and artifact security.
07 — Event-Driven Variant
Event-driven ingestion with Lambda/EventBridge patterns for file arrival, retries and operational notifications.
08 — Near-Production AWS Platform
Multi-layer data lake, serving, analytics, MWAA orchestration, monitoring, security and DR.
09 — Final Production Blueprint
Production-grade global investment banking market-data platform with real-time + daily batch, governance, DR and automated reports.
06 / FOUNDATION
Education & certifications.
Bachelor of Arts (Economics), University of Pune.
IBM Data Analyst Professional CertificateCoursera
Data Science & Analytics SpecialistITVEDANT
Hardware & Networking TechnicianSilicon Valley Institute
07 / CONTACT
Let's talk about data engineering.
For Data Engineer roles, ETL/ELT projects, cloud data platforms, analytics engineering or architecture discussions, send a message through the form.