Data Engineer - Cloud Data Platform Specialist

Phanideep P

Email: ***********@*****.***

Mobile: 980-***-****

LinkedIn: https://www.linkedin.com/in/gnana-phanideep-p-19a092258/ Data Engineer

PROFESSIONAL SUMMARY

Experienced data engineer with five years delivering data platforms across banking, insurance, and pharma, aligning stakeholder requirements with scalable pipelines and actionable analytics solutions.

Hands-on expertise across Python, SQL, Spark, Snowflake, Databricks, Airflow, Kafka, and CI/CD, designing end- to-end batch and streaming pipelines powering analytics, reporting, and machine-learning workloads.

Proficient with AWS, Azure, and GCP cloud ecosystems, building secure data lakes and lakehouse architectures with governance, lineage, observability, and automation supporting data consumers.

Focused on outcomes by improving data quality, reliability, performance, and compliance, partnering with stakeholders to deliver curated datasets, models, dashboards, and self-service analytics capabilities.

Facilitated team meetings using excellent written oral communication skills, enhancing collaboration and project outcomes.

Streamlined workflows through passion automation continual process improvement, boosting productivity by 30%. TECHNICAL SKILLS

Cloud Platforms - AWS (EC2, Lambda, Glue, S3, Kinesis, IAM, EKS, Redshift), Azure (ADF, Synapse, Azure SQL, Entra ID, Key Vault), GCP (BigQuery, GKE, Cloud Storage)

Infrastructure as Code (IaC) - Terraform, Ansible, ARM Templates, Bicep, CloudFormation, Jenkins, Azure DevOps

Monitoring and Incident Response - New Relic, AWS CloudWatch, Azure Monitor, ServiceNow, RCA, SLA Management

Security and Compliance - IAM, Encryption, NIST 800-53, CIS Benchmarks, PCI-DSS, RBAC, Key Vault, Audit Logging

CI/CD and DevOps - Jenkins, GitHub Actions, Git, GitLab, CodePipeline, CI/CD Pipelines, Shell Scripting

Programming & Scripting - Python, SQL, Bash, PowerShell, Perl

Data Engineering - AWS Glue, Azure Data Factory, DBT, Apache Kafka, Spark, Hive, GCP Dataflow, ETL, data flows

Databases - Redshift, Snowflake, Azure SQL, PostgreSQL, MongoDB, MySQL, Oracle, Oracle Exadata

Dashboards and Visualization - Power BI, Tableau, Looker, AWS QuickSight

Data Warehousing - data warehouses

System Administration - Linux-based processes, Unix file systems, Linux environment setup

Tools and Platforms - Informatica

PROFESSIONAL EXPERIENCE

Cadence Bank June 2024 – Present

Senior Data Engineer

Designed cloud-native banking data lakehouse on AWS and Databricks with Delta Lake, consolidating banking, credit risk, and regulatory reporting data into governed medallion layers.

Developed PySpark and SQL pipelines in Airflow, ingesting feeds into Snowflake, enriching customer profiles for analytics dashboards built in Power BI for treasury stakeholders.

Implemented Kafka-based ingestion for fraud monitoring streams, persisting features into Snowflake, enabling machine learning models to detect anomalous card activity and reduce customer losses.

Engineered CI/CD pipelines with GitHub Actions and Terraform, automating deployment of Databricks jobs, Airflow DAGs, and Snowflake objects across environments aligned with change controls.

Optimized SQL and Spark workloads on Snowflake and Databricks, tuning partitions, caching, and clustering strategies to meet reporting SLAs for liquidity, capital, and stress-testing.

Revolutionized data flows by optimizing ETL/database load/extract processes with Oracle and Informatica, resulting in a 30% increase in data throughput and reducing processing time by 40%. Globe Life Inc July 2023 – May 2024

Data Engineer

Automated ELT pipelines for policy, underwriting, and claims data with Azure Data Factory, Databricks, and Snowflake, improving reserving calculations and reporting across insurance portfolios.

Orchestrated streaming ingestion from on-premises policy systems into Azure lakehouse, applying Delta Lake patterns to unify incremental feeds for pricing analytics and supervisory reporting.

Integrated demographic and behavioral datasets with internal policy data via Python and SQL, creating feature stores strengthening risk underwriting models and automating eligibility decisions.

Standardized data quality checks in Great Expectations and dbt, embedding reconciliation rules, reducing validation effort for teams and improving trust in data for reporting.

Configured Power BI models and row-level security over Snowflake, enabling self-service analytics for partners and ensuring policyholder attributes complied with governance and privacy standards.

Engineered Linux environment setup and Unix file systems, including basics mount types and permissions, enhancing system reliability and achieving 99.9% uptime for critical data warehousing operations. Apollo Munich Health Insurance July 2021 – July 2022 Data Engineer

Modernized healthcare reporting platform by migrating SAS and on-premises SQL Server workloads into GCP BigQuery and Dataflow pipelines, reducing batch latency for claims metrics.

Consolidated member eligibility, provider, and medical claims feeds into dimensional models in BigQuery, improving accuracy of health insurance analytics and supporting population health initiatives.

Streamlined ingestion of HL7 and healthcare data formats through Python parsers and Airflow orchestration, enabling standardized structures for analytics on pathways and clinical performance.

Enhanced data quality by implementing validation frameworks in Python and SQL, capturing patterns across claims and provider data, reducing incidents impacting healthcare analytics consumers.

Secured sensitive health data across GCP storage and BigQuery by enforcing encryption, tokenization, and access controls aligned with governance, audit requirements, and healthcare regulations. Divis Laboratories Limited March 2020 – June 2021

Data Engineer

Validated laboratory and manufacturing data pipelines built with Python, Spark, and Snowflake, establishing reconciliation controls against source systems supporting pharma analytics and regulatory readiness.

Analyzed historian, lab, and ERP datasets to build dimensional models in Snowflake and Power BI, enabling manufacturing dashboards and trend analysis for pharmaceutical lines.

Coordinated data integration between LIMS, MES, and SAP systems on AWS, designing ingestion frameworks ensuring batch genealogy, traceability, and deviation tracking across production environments.

Refactored PL/SQL and SSIS workflows into Python and Spark jobs scheduled through Airflow, reducing maintenance overhead while improving reliability of manufacturing pipelines for reporting.

Monitored laboratory and manufacturing pipelines with observability dashboards in Grafana and Prometheus, surfacing data freshness, schema drift, and failure trends to teams for remediation. CERTIFICATIONS

Microsoft Certified: Azure Data Engineer Associate

AWS Certified: Data Engineer Associate

EDUCATION

Master's in Computer Science - Wright State University

Bachelor's in Computer Science - Vignan’s Lara Institute of Technology and Science

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